Risk prediction models for mortality in community-acquired pneumonia: a systematic review

CRB-65 for Risk Stratification and Prediction of Prognosis in Pulmonary Embolism

BACKGROUND

Pulmonary embolism (PE) is accompanied by high morbidity and mortality. The search for simple and easily assessable risk stratification scores with favourable effectiveness is still ongoing, and prognostic performance of the CRB-65 score in PE might promising.

METHODS

The German nationwide inpatient sample was used for this study. All patient cases of patients with PE in Germany 2005-2020 were included and stratified for CRB-65 risk class: low-risk group (CRB-65-score 0 points) vs. high-risk group (CRB-65-score ≥1 points).

RESULTS

Overall, 1,373,145 patient cases of patients with PE (76.6% aged ≥65 years, 47.0% females) were included. Among these, 1,051,244 patient cases (76.6%) were classified as high-risk according to CRB-65 score (≥1 points). The majority of high-risk patients according to CRB-65 score were females (55.8%). Additionally, high-risk patients according to CRB-65 score showed an aggravated comorbidity profile with increased Charlson comorbidity index (5.0 [IQR 4.0-7.0] vs. 2.0 [0.0-3.0], p < 0.001). In-hospital case fatality (19.0% vs. 3.4%, p < 0.001) and MACCE (22.4% vs. 5.1%, p < 0.001) occurred distinctly more often in PE patients of the high-risk group according to CRB-65 score (≥1 points) compared to the low-risk group (= 0 points). The CRB-65 high-risk class was independently associated with in-hospital death (OR 5.53 [95%CI 5.40-5.65], p < 0.001) as well as MACCE (OR 4.31 [95%CI 4.23-4.40], p < 0.001).

CONCLUSIONS

Risk stratification with CRB-65 score was helpful for identifying PE patients being at higher risk of adverse in-hospital events. The high-risk class according to CRB-65 score (≥1 points) was independently associated with a 5.5-fold increased occurrence of in-hospital death.

Development and validation of a new scoring system for prognostic prediction of community-acquired pneumonia in older adults

The discriminative power of CURB-65 for mortality in community-acquired pneumonia (CAP) is suspected to decrease with age. However, a useful prognostic prediction model for older patients with CAP has not been established. This study aimed to develop and validate a new scoring system for predicting mortality in older patients with CAP. We recruited two prospective cohorts including patients aged ≥ 65 years and hospitalized with CAP. In the derivation (n = 872) and validation cohorts (n = 1,158), the average age was 82.0 and 80.6 years and the 30-day mortality rate was 7.6% (n = 66) and 7.4% (n = 86), respectively. A new scoring system was developed based on factors associated with 30-day mortality, identified by multivariate analysis in the derivation cohort. This scoring system named CHUBA comprised five variables: confusion, hypoxemia (SpO₂ ≤ 90% or PaO₂ ≤ 60 mmHg), blood urea nitrogen ≥ 30 mg/dL, bedridden state, and serum albumin level ≤ 3.0 g/dL. With regard to 30-day mortality, the area under the receiver operating characteristic curve for CURB-65 and CHUBA was 0.672 (95% confidence interval, 0.607–0.732) and 0.809 (95% confidence interval, 0.751–0.856; P < 0.001), respectively. The effectiveness of CHUBA was statistically confirmed in the external validation cohort. In conclusion, a simpler novel scoring system, CHUBA, was established for predicting mortality in older patients with CAP.

Clinical characteristics and in-hospital mortality of patients with COVID-19 in Chile: A prospective cohort study

AIMS OF THIS STUDY

To describe the Latin American population affected by COVID-19, and to determine relevant risk factors for in-hospital mortality.

METHODS

We prospectively registered relevant clinical, laboratory, and radiological data of adult patients with COVID-19, admitted within the first 100 days of the pandemic from a single teaching hospital in Santiago, Chile. The primary outcome was in-hospital mortality. Secondary outcomes included the need for respiratory support and pharmacological treatment, among others. We combined the chronic disease burden and the severity of illness at admission with predefined clinically relevant risk factors. Cox regression models were used to identify risk factors for in-hospital mortality.

RESULTS

We enrolled 395 adult patients, their median age was 61 years; 62.8% of patients were male and 40.1% had a Modified Charlson Comorbidity Index (MCCI) ≥5. Their median Sequential Organ Failure Assessment (SOFA) score was 3; 34.9% used a high-flow nasal cannula and 17.5% required invasive mechanical ventilation. The in-hospital mortality rate was 14.7%. In the multivariate analysis, were significant risk factors for in-hospital mortality: MCCI ≥5 (HR 4.39, P < .001), PaO₂ /FiO₂ ratio ≤200 (HR 1.92, P = .037), and advanced chronic respiratory disease (HR 3.24, P = .001); pre-specified combinations of these risk factors in four categories was associated with the outcome in a graded manner.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The relationship between multiple prognostic factors has been scarcely reported in Latin American patients with COVID-19. By combining different clinically relevant risk factors, we can identify COVID-19 patients with high-, medium- and low-risk of in-hospital mortality.

Clinical characteristics and prognostic risk factors of mortality in patients with interstitial lung diseases and viral infection: a retrospective cohort study

Introduction. Patients with interstitial lung disease (ILD) who subsequently develop a viral infection have high rates of morbidity and mortality.Hypothesis/Gap Statement. Few large-scale epidemiological studies have investigated potential prognostic factors for morbidity and mortality in this patient group.Aim. To evaluate the risk factors for morbidity and mortality in hospitalized patients with ILD and viral infection, as well as the clinical characteristics.Methodology. This retrospective cohort study included patients with ILD who were hospitalized for a viral infection in two tertiary academic hospitals in China, between 1 January 2013 and 31 December 2019. We analysed the prevalence of comorbidities, clinical characteristics, 30 day mortality rates, and prognostic risk factors.Results. A total of 282 patients were included; 195 and 87 were immunocompromised and immunocompetent, respectively. The most common underlying interstitial diseases were idiopathic pulmonary fibrosis (42.9 %) and connective tissue disease (36.9 %). The 30 day mortality rate was 20.6 %. During the influenza season, an increase in influenza virus (IFV) (25.7 %), respiratory syncytial virus (14.9 %) and cytomegalovirus (CMV) (11.3 %) cases was observed in the immunocompromised group. The most frequently detected virus in the immunocompetent group was IFV (44.8 %), followed by respiratory syncytial virus (11.5 %), and human rhinovirus (9.2 %). During the non-influenza season, CMV (34.4 %) was the main virus detected in the immunocompromised group. The 30 day mortality rates of non-IFV patients were higher than those of IFV patients. Older age (>60 years), respiratory failure, persistent lymphocytopenia, invasive mechanical ventilation and non-IFV virus infection were significantly associated with increased 30 day mortality.Conclusion. Patients with ILD who develop viral infection have high rates of morbidity and mortality, which are associated with increased age (>60 years), respiratory failure, mechanical ventilation, persistent lymphocytopenia and non-IFV virus infection. These risk factors should be carefully considered when determining treatment strategies for this patient population.

Computerized Mortality Prediction for Community-acquired Pneumonia at 117 Veterans Affairs Medical Centers

Rationale: Computerized severity assessment for community-acquired pneumonia could improve consistency and reduce clinician burden. Objectives: To develop and compare 30-day mortality-prediction models using electronic health record data, including a computerized score with all variables from the original Pneumonia Severity Index (PSI) except confusion and pleural effusion ("ePSI score") versus models with additional variables. Methods: Among adults with community-acquired pneumonia presenting to emergency departments at 117 Veterans Affairs Medical Centers between January 1, 2006, and December 31, 2016, we compared an ePSI score with 10 novel models employing logistic regression, spline, and machine learning methods using PSI variables, age, sex and 26 physiologic variables as well as all 69 PSI variables. Models were trained using encounters before January 1, 2015; tested on encounters during and after January 1, 2015; and compared using the areas under the receiver operating characteristic curve, confidence intervals, and patient event rates at a threshold PSI score of 970. Results: Among 297,498 encounters, 7% resulted in death within 30 days. When compared using the ePSI score (confidence interval [CI] for the area under the receiver operating characteristic curve, 0.77-0.78), performance increased with model complexity (CI for the logistic regression PSI model, 0.79-0.80; CI for the boosted decision-tree algorithm machine learning PSI model using the Extreme Gradient Boosting algorithm [mlPSI] with the 19 original PSI factors, 0.83-0.85) and the number of variables (CI for the logistic regression PSI model using all 69 variables, 0.84-085; CI for the mlPSI with all 69 variables, 0.86-0.87). Models limited to age, sex, and physiologic variables also demonstrated high performance (CI for the mlPSI with age, sex, and 26 physiologic factors, 0.84-0.85). At an ePSI score of 970 and a mortality-risk cutoff of <2.7%, the ePSI score identified 31% of all patients as being at "low risk"; the mlPSI with age, sex, and 26 physiologic factors identified 53% of all patients as being at low risk; and the mlPSI with all 69 variables identified 56% of all patients as being at low risk, with similar rates of mortality, hospitalization, and 7-day secondary hospitalization being determined. Conclusions: Computerized versions of the PSI accurately identified patients with pneumonia who were at low risk of death. More complex models classified more patients as being at low risk of death and as having similar adverse outcomes.

Artificial Intelligence Is Reshaping Healthcare amid COVID-19: A Review in the Context of Diagnosis & Prognosis

Preventing respiratory failure is crucial in a large proportion of COVID-19 patients infected with SARS-CoV-2 virus pneumonia termed as Novel Coronavirus Pneumonia (NCP). Rapid diagnosis and detection of high-risk patients for effective interventions have been shown to be troublesome. Using a large, computed tomography (CT) database, we developed an artificial intelligence (AI) parameter to diagnose NCP and distinguish it from other kinds of pneumonia and traditional controls. The literature was studied and analyzed from diverse assets which include Scopus, Nature medicine, IEEE, Google scholar, Wiley Library, and PubMed. The search terms used were 'COVID-19', 'AI', 'diagnosis', and 'prognosis'. To strengthen the overall performance of AI in COVID-19 diagnosis and prognosis, we segregated several components to perceive threats and opportunities, as well as their inter-dependencies that affect the healthcare sector. This paper seeks to pick out the crucial fulfillment of factors for AI with inside the healthcare sector in the Indian context. Using critical literature review and experts' opinion, a total of 11 factors affecting COVID-19 diagnosis and prognosis were detected, and we eventually used an interpretive structural model (ISM) to build a framework of interrelationships among the identified factors. Finally, the matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis resulted the driving and dependence powers of these identified factors. Our analysis will help healthcare stakeholders to realize the requirements for successful implementation of AI.

Charlson Comorbidity Index and other predictors of in-hospital mortality among adults with community-acquired pneumonia

Objective:

To compare the performance of Charlson Comorbidity Index (CCI) with those of the mental Confusion, Urea, Respiratory rate, Blood pressure, and age = 65 years (CURB-65) score and the Pneumonia Severity Index (PSI) as predictors of all-cause in-hospital mortality in patients with community-acquired pneumonia (CAP).

Methods:

This was a cohort study involving hospitalized patients with CAP between April of 2014 and March of 2015. Clinical, laboratory, and radiological data were obtained in the ER, and the scores of CCI, CURB-65, and PSI were calculated. The performance of the models was compared using ROC curves and AUCs (95% CI).

Results:

Of the 459 patients evaluated, 304 met the eligibility criteria. The all-cause in-hospital mortality rate was 15.5%, and 89 (29.3%) of the patients were admitted to the ICU. The AUC for the CCI was significantly greater than those for CURB-65 and PSI (0.83 vs. 0.73 and 0.75, respectively).

Conclusions:

In this sample of hospitalized patients with CAP, CCI was a better predictor of all-cause in-hospital mortality than were the PSI and CURB-65.

Comparison of pneumonia severity scores for patients diagnosed with pneumonia in emergency department

Background & objectives:

Sepsis due to pneumonia or pneumonia itself is one of the main causes of deaths in patients despite the advanced treatment methods. The optimal prognostic tool in pneumonia is still not clear. This study was aimed to compare the pneumonia severity scores and the possibility of using the new scores in patients who were diagnosed with pneumonia in the emergency department.

Methods:

Demographic data, laboratory and imaging results, confusion, elevated blood urea nitrogen, respiratory rate and blood pressure plus age ≥65 yr (CURB-65), pneumonia severity index (PSI), national early warning score (NEWS), NEWS-lactate (NEWS-L) scores, hospitalization, referral, discharge and 30-day mortality of patients who were diagnosed with pneumonia in emergency department were recorded.

Results:

A total of 250 patients were included in the study. The most successful score in predicted mortality was found to be NEWS-L. This was followed by NEWS, CURB-65 and PSI, respectively. Most successful scores in anticipation of admission to the intensive care unit were NEWS-L followed by NEWS. This was followed by CURB-65 and PSI scores, respectively. The most successful score in anticipation of hospital admission was NEWS-L, followed by NEWS, CURB-65 and PSI, respectively. There was a significant difference between all pneumonia severity scores of the patients who died and survived within 30 days. There was a significant difference between the scores of patients in intensive care unit (ICU) and service, compared to non-ICU patients.

Interpretation & conclusions:

NEWS-L score was found to be the most successful score in predicting mortality, ICU admission and hospitalization requirement. Both NEWS-L and NEWS scores can be used in determining the mortality, need for hospitalization and intensive care of the patients with pneumonia in the emergency department.

Aetiology and prognostic risk factors of mortality in patients with pneumonia receiving glucocorticoids alone or glucocorticoids and other immunosuppressants: a retrospective cohort study

OBJECTIVES

Long-term use of high-dose glucocorticoids can lead to severe immunosuppression and increased risk of treatment-resistant pneumonia and mortality. We investigated the aetiology and prognostic risk factors of mortality in hospitalised patients who developed pneumonia while receiving glucocorticoid therapy alone or glucocorticoid and other immunosuppressant therapies.

DESIGN

Retrospective cohort study.

SETTING

Six secondary and tertiary academic hospitals in China.

PARTICIPANTS

Patients receiving glucocorticoids who were hospitalised with pneumonia between 1 January 2013 and 31 December 2019.

MAIN OUTCOMES

We analysed the prevalence of comorbidities, microbiology, antibiotic susceptibility patterns, 30-day and 90-day mortality and prognostic risk factors.

RESULTS

CONCLUSIONS: A total of 716 patients were included, with pneumonia pathogens identified in 69.8% of patients. Significant morbidities occurred, including respiratory failure (50.8%), intensive care unit transfer (40.8%) and mechanical ventilation (36%), with a 90-day mortality of 26.0%. Diagnosis of pneumonia occurred within 6 months of glucocorticoid initiation for 69.7% of patients with Cytomegalovirus (CMV) pneumonia and 79.0% of patients with Pneumocystis jirovecii pneumonia (PCP). Pathogens, including Pneumocystis, CMV and multidrug-resistant bacteria, were identified more frequently in patients with persistent lymphocytopenia and high-dose glucocorticoid treatment (≥30 mg/day of prednisolone or equivalent within 30 days before admission). The 90-day mortality was significantly lower for non-CMV viral pneumonias than for PCP (p<0.05), with a similar mortality as CMV pneumonias (24.2% vs 38.1% vs 27.4%, respectively). Cox regression analysis indicated several independent negative predictors for mortality in this patient population, including septic shock, respiratory failure, persistent lymphocytopenia, interstitial lung disease and high-dose glucocorticoid use.Patients who developed pneumonia while receiving glucocorticoid therapy experienced high rates of opportunistic infections, with significant morbidity and mortality. These findings should be carefully considered when determining treatment strategies for this patient population.

Community-Acquired Pneumonia. Spanish Society of Pulmonology and Thoracic Surgery (SEPAR) Guidelines. 2020 Update

The guidelines for community-acquired pneumonia, last published in 2010, have been updated to provide recommendations based on a critical summary of the latest literature to help health professionals make the best decisions in the care of immunocompetent adult patients. The methodology was based on 6 PICO questions (on etiological studies, assessment of severity and decision to hospitalize, antibiotic treatment and duration, and pneumococcal conjugate vaccination), agreed by consensus among a working group of pulmonologists and an expert in documentation science and methodology. A comprehensive review of the literature was performed for each PICO question, and these were evaluated in in-person meetings. The American Thoracic Society guidelines were published during the preparation of this paper, so the recommendations of this association were also evaluated. We concluded that the etiological source of the infection should be investigated in hospitalized patients who have suspected resistance or who fail to respond to treatment. Prognostic scales, such as PSI, CURB 65, and CRB65, are useful for assessing severity and the decision to hospitalize. Different antibiotic regimens are indicated, depending on the treatment setting - outpatient, hospital, or intensive care unit - and the resistance of PES microorganisms should be calculated. The minimum duration of antibiotic treatment should be 5 days, based on criteria of clinical stability. Finally, we reviewed the indication of the 13-valent conjugate vaccine in immunocompetent patients with risk factors and comorbidity.

Autonomic Nervous System Activity for Risk Stratification of Emergency Patients With Pneumonia

BACKGROUND

Community-acquired pneumonia (CAP) causes appreciable morbidity and mortality in adults, especially in those ≥65 years of age. At hospital admission, an immediate and reliable risk assessment is necessary to detect patients with possible fatal outcome.

OBJECTIVE

We aimed to evaluate markers of the autonomic nervous system based on an electrocardiogram to predict mortality in patients with CAP.

METHODS

For this purpose, the deceleration capacity (DC) of heart rate was calculated in 253 patients who presented to the emergency department with CAP. The 30-day mortality rate was defined as the primary endpoint (PEP). The secondary endpoint was the total mortality within 180 days.

RESULTS

PEP was reached in 33 patients (13%). The DC, measured in milliseconds, was significantly lower in patients who reached the PEP than in those who did not (2.3 ± 1.5 ms vs. 3.6 ± 2.3 ms, p = 0.004). The DC was also significantly lower in nonsurvivors than in survivors at the time of the secondary endpoint (2.3 ± 1.5 ms vs. 3.7 ± 2.4 ms, p < 0.001). Our results indicate that DC is an independent predictor of 30- and 180-day mortality.

CONCLUSION

DC was independently associated with death from CAP in our study. As a practical consequence, DC could be useful in triage decisions. Patients with certain high risks could benefit from adjuvant treatment and special medical attention.

Six underlying health conditions strongly influence mortality based on pneumonia severity in an ageing population of Japan: a prospective cohort study

Background

Mortality prediction of pneumonia by severity scores in patients with multiple underlying health conditions has not fully been investigated. This prospective cohort study is to identify mortality-associated underlying health conditions and to analyse their influence on severity-based pneumonia mortality prediction.

Methods

Adult patients with community-acquired pneumonia or healthcare-associated pneumonia (HCAP) who visited four community hospitals between September 2011 and January 2013 were enrolled. Candidate underlying health conditions, including demographic and clinical characteristics, were incorporated into the logistic regression models, along with CURB (confusion, elevated urea nitrogen, tachypnoea, and hypotension) score as a measure of disease severity. The areas under the receiver operating characteristic curves (AUROC) of the predictive index based on significant underlying health conditions was compared to that of CURB65 (CURB and age ≥ 65) score or Pneumonia severity index (PSI). Mortality association between disease severity and the number of underlying health conditions was analysed.

Results

In total 1772 patients were eligible for analysis, of which 140 (7.9%) died within 30 days. Six underlying health conditions were independently associated: home care (adjusted odds ratio, 5.84; 95% confidence interval, CI, 2.28–14.99), recent hospitalization (2.21; 1.36–3.60), age ≥ 85 years (2.15; 1.08–4.28), low body mass index (1.99, 1.25–3.16), neoplastic disease (1.82; 1.17–2.85), and male gender (1.78; 1.16–2.75). The predictive index based on these conditions alone had a significantly or marginally higher AUROC than that based on CURB65 score (0.78 vs 0.66, p = 0.02) or PSI (0.78 vs 0.71, p = 0.05), respectively. Compared to this index, the AUROC of the total score consisting of six underlying health conditions and CURB score (range 0–10) did not improve mortality predictions (p = 0.3). In patients with one or less underlying health conditions, the mortality was discretely associated with severe pneumonia (CURB65 ≥ 3) (risk ratio: 7.24, 95%CI: 3.08–25.13), whereas in patients with 2 or more underlying health conditions, the mortality association with severe pneumonia was not detected (risk ratio: 1.53, 95% CI: 0.94–2.50).

Conclusions

Mortality prediction based on pneumonia severity scores is highly influenced by the accumulating number of underlying health conditions in an ageing society. The validation using a different cohort is necessary to generalise the conclusion.

Electronic supplementary material

The online version of this article (10.1186/s12890-018-0648-y) contains supplementary material, which is available to authorized users.

Predicting the Risk of Readmission in Pneumonia. A Systematic Review of Model Performance

RATIONALE

Predicting which patients are at highest risk for readmission after hospitalization for pneumonia could enable hospitals to proactively reallocate scarce resources to reduce 30-day readmissions.

OBJECTIVES

To synthesize the available literature on readmission risk prediction models for adults who are hospitalized because of pneumonia and describe their performance.

METHODS

We systematically searched Ovid MEDLINE, Embase, The Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature databases from inception through July 2015. We included studies of adults discharged with pneumonia that developed or validated a model that predicted hospital readmission. Two independent reviewers abstracted data and assessed the risk of bias.

MEASUREMENTS AND MAIN RESULTS

Of 992 citations reviewed, 7 studies met inclusion criteria, which included 11 unique risk prediction models. All-cause 30-day readmission rates ranged from 11.8 to 20.8% (median, 17.3%). Model discrimination (C statistic) ranged from 0.59 to 0.77 (median, 0.63) with the highest-quality, best-validated model, the Centers for Medicare and Medicaid Services Pneumonia Administrative Model performing modestly (C Statistic of 0.63 in 4 separate multicenter cohorts). The best performing model (C statistic of 0.77) was a single-site study that lacked internal validation. The models had adequate calibration, with patients predicted as high risk for readmission having a higher average observed readmission rate than those predicted to be low risk. None of the studies included pneumonia illness severity scores, and only one included measures of in-hospital clinical trajectory and stability on discharge, robust predictors of readmission.

CONCLUSIONS

We found a limited number of validated pneumonia-specific readmission models, and their predictive ability was modest. To improve predictive accuracy, future models should include measures of pneumonia illness severity, hospital complications, and stability on discharge.

Predicting 30-Day Pneumonia Readmissions Using Electronic Health Record Data

BACKGROUND

Readmissions after hospitalization for pneumonia are common, but the few risk-prediction models have poor to modest predictive ability. Data routinely collected in the electronic health record (EHR) may improve prediction.

OBJECTIVE

To develop pneumonia-specific readmission risk-prediction models using EHR data from the first day and from the entire hospital stay ("full stay").

DESIGN

Observational cohort study using stepwise-backward selection and cross-validation.

SUBJECTS

Consecutive pneumonia hospitalizations from 6 diverse hospitals in north Texas from 2009-2010.

MEASURES

All-cause nonelective 30-day readmissions, ascertained from 75 regional hospitals.

RESULTS

Of 1463 patients, 13.6% were readmitted. The first-day pneumonia-specific model included sociodemographic factors, prior hospitalizations, thrombocytosis, and a modified pneumonia severity index; the full-stay model included disposition status, vital sign instabilities on discharge, and an updated pneumonia severity index calculated using values from the day of discharge as additional predictors. The full-stay pneumonia-specific model outperformed the first-day model (C statistic 0.731 vs 0.695; P = 0.02; net reclassification index = 0.08). Compared to a validated multi-condition readmission model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores, the full-stay pneumonia-specific model had better discrimination (C statistic range 0.604-0.681; P < 0.01 for all comparisons), predicted a broader range of risk, and better reclassified individuals by their true risk (net reclassification index range, 0.09-0.18).

CONCLUSIONS

EHR data collected from the entire hospitalization can accurately predict readmission risk among patients hospitalized for pneumonia. This approach outperforms a first-day pneumonia-specific model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores. Journal of Hospital Medicine 2017;12:209-216.

Tools for outcome prediction in patients with community acquired pneumonia

INTRODUCTION

Community-acquired pneumonia (CAP) is one of the most common causes of mortality world-wide. The mortality rate of patients with CAP is influenced by the severity of the disease, treatment failure and the requirement for hospitalization and/or intensive care unit (ICU) management, all of which may be predicted by biomarkers and clinical scoring systems. Areas covered: We review the recent literature examining the efficacy of established and newly-developed clinical scores, biological and inflammatory markers such as C-Reactive protein (CRP), procalcitonin (PCT) and Interleukin-6 (IL-6), whether used alone or in conjunction with clinical severity scores to assess the severity of CAP, predict treatment failure, guide acute in-hospital or ICU admission and predict mortality. Expert commentary: The early prediction of treatment failure using clinical scores and biomarkers plays a developing role in improving survival of patients with CAP by identifying high-risk patients requiring hospitalization or ICU admission; and may enable more efficient allocation of resources. However, it is likely that combinations of scoring systems and biomarkers will be of greater use than individual markers. Further larger studies are needed to corroborate the additive value of these markers to clinical prediction scores to provide a safer and more effective assessment tool for clinicians.

Plasma cytokines eotaxin, MIP-1α, MCP-4, and vascular endothelial growth factor in acute lower respiratory tract infection

Major overlaps of clinical characteristics and the limitations of conventional diagnostic tests render the initial diagnosis and clinical management of pulmonary disorders difficult. In this pilot study, we analyzed the predictive value of eotaxin, macrophage inflammatory protein 1 alpha (MIP-1α), monocyte chemoattractant protein 4 (MCP-4), and vascular endothelial growth factor (VEGF) in 40 patients hospitalized with acute lower respiratory tract infections (LRTI). The cytokines contribute to the pathogenesis of several inflammatory respiratory diseases, indicating a potential as markers for LRTI. Patients were stratified according to etiology and severity of LRTI, based on baseline C-reactive protein and CURB-65 scores. Using a multiplex immunoassay of plasma, levels of eotaxin and MCP-4 were shown to increase from baseline until day 6 after admission to hospital. The four cytokines were unable to predict the etiology and severity. Eotaxin and MCP-4 were significantly lower in patients with C-reactive protein ≥100, and MIP-1α was significantly higher in the patients with CURB-65 > 3, but the predictive power was low. In conclusion, further evaluation, including more patients, is required to assess the full potential of eotaxin, MCP-4, MIP-1α, and VEGF as biomarkers for LRTI because of their low predictive power and a high interindividual variation of cytokine levels.

The Use of Guidelines for Lower Respiratory Tract Infections in Tanzania: A Lesson from Kilimanjaro Clinicians

Background:

Evaluations of the guidelines for the management of Lower Respiratory Tract Infections (LRTI) Sub-Saharan Africa, particularly in Tanzania is scant.

Aim:

The aim of the study was to assess the usefulness of the current Tanzanian treatment guideline for the management lower respiratory tract infection.

Subjects and Methods:

A descriptive cross sectional study in 11 hospitals of different levels in the Kilimanjaro region Data were collected from May 2012 to July 2012 by semi-structured interview for clinicians using 2 dummy cases for practical assessment. Data were analyzed by STATA v11 (StataCorp, TX, USA). Qualitative narratives from the interviews were translated, transcribed then coded by colors into meaningful themes.

Results:

A variety of principles for diagnosing and managing LRTI were demonstrated by 53 clinicians of Kilimanjaro. For the awareness, 67.9% (36/53) clinicians knew their responsibility to use Standard Treatment Guideline for managing LRTI. The content derived from Standard Treatment Guideline could be cited by 11.3% of clinicians (6/53) however they all showed concern of gaps in the guideline. Previous training in the management of patients with LRTI was reported by 25.9% (14/53), majority were pulmonary TB related. Correct microorganisms causing different forms of LRTI were mentioned by 11.3% (6/53). Exact cause of Atypical pneumonia and Q fever as an example was stated by 13.0% (7/53) from whom the need of developing the guideline for LRTI was explicitly elaborated.

Conclusion:

The current guidelines have not been used effectively for the management of LRTI in Tanzania. There is a need to review its content for the current practical use.

Validation of modified early warning score using serum lactate level in community-acquired pneumonia patients. The National Early Warning Score-Lactate score

STUDY OBJECTIVE

The aim of this study was to investigate the prognostic prediction power of a newly introduced early warning score modified by serum lactate level, the National Early Warning Score-Lactate (NEWS-L) score, among community-acquired pneumonia (CAP) patients. We also compared the NEWS-L score with the pneumonia severity index (PSI) and CURB-65.

METHODS

We designed a retrospective observational study and collected data on confirmed adult CAP patients who visited the study hospital between October 2013 and September 2014. Variables relevant to, the NEWS-L score, PSI, and CURB-65 were extracted from electronic medical records. Survival status at hospital discharge was determined in the same manner. The NEWS-L score was calculated as NEWS-L=NEWS+serum lactate level (mmol/L). The NEWS-L was divided into quartiles. The ability to predict mortality was assessed through area under the receiver operating characteristic curve analysis and calibration analysis.

RESULTS

A total of 553 patients were enrolled, and the inpatient mortality rate was 10.8% (n=60). Mortality rates increased incrementally in conjunction with the NEWS-L quartiles: first quartile, 2.2%; second quartile, 7.9%; third quartile, 9.6%; and fourth quartile, 23.9%. The area under the receiver operating characteristic curve of the NEWS-L score was 0.73 (95% confidence interval [CI], 0.66-0.80), which showed no significant difference from that of the PSI (0.68; 95% CI, 0.61-0.76; P=.28) and CURB-65 (0.66; 95% CI, 0.59-0.73; P=.06).

CONCLUSIONS

The newly introduced early warning score modified by serum lactate level, NEWS-L score, was comparable to PSI and CURB-65, for predicting inpatient mortality among adult CAP patients.

Community-acquired pneumonia (CAP) hospitalizations and deaths: is there a role for quality improvement through inter-hospital comparisons?

OBJECTIVE

To assess between-hospital variations in standardized in-hospital mortality ratios of community-acquired pneumonia (CAP), and identify possible leads for quality improvement.

DESIGN

We used an administrative database to estimate standardized in-hospital mortality ratios for 111 Belgian hospitals, by carrying out a set of hierarchical logistic regression models, intended to disentangle therapeutic attitudes and biases. To facilitate the detection of false-negative/positive results, we added an inconclusive zone to the funnel plots, derived from the results of the study. Data quality was validated by comparison with (i) alternative data from the largest Belgian Sickness Fund, (ii) published German hospital data and (iii) the results of an on-site audit.

SETTING

All Belgian hospital discharge records from 2004 to 2007.

STUDY PARTICIPANTS

A total of 111 776 adult patients were admitted for CAP.

MAIN OUTCOME MEASURE

Risk-adjusted standardized in-hospital mortality ratios.

RESULTS

Out of the 111 hospitals, we identified five and six outlying hospitals, with standardized mortality ratios of CAP consistently on the extremes of the distribution, as providing possibly better or worse care, respectively, and 18 other hospitals as having possible quality weaknesses/strengths. At the individuals' level of the analysis, adjusted odds ratios showed the paramount importance of old age, comorbidity and mechanical ventilation. The data compared well with the different validation sources.

CONCLUSIONS

Despite the limitations inherent to administrative data, it seemed possible to establish inter-hospital differences in standardized in-hospital mortality ratios of CAP and to identify leads for quality improvement. Monitoring is needed to assess progress in quality.

Risk Prediction of One-Year Mortality in Patients with Cardiac Arrhythmias Using Random Survival Forest

Existing models for predicting mortality based on traditional Cox proportional hazard approach (CPH) often have low prediction accuracy. This paper aims to develop a clinical risk model with good accuracy for predicting 1-year mortality in cardiac arrhythmias patients using random survival forest (RSF), a robust approach for survival analysis. 10,488 cardiac arrhythmias patients available in the public MIMIC II clinical database were investigated, with 3,452 deaths occurring within 1-year followups. Forty risk factors including demographics and clinical and laboratory information and antiarrhythmic agents were analyzed as potential predictors of all-cause mortality. RSF was adopted to build a comprehensive survival model and a simplified risk model composed of 14 top risk factors. The built comprehensive model achieved a prediction accuracy of 0.81 measured by c-statistic with 10-fold cross validation. The simplified risk model also achieved a good accuracy of 0.799. Both results outperformed traditional CPH (which achieved a c-statistic of 0.733 for the comprehensive model and 0.718 for the simplified model). Moreover, various factors are observed to have nonlinear impact on cardiac arrhythmias prognosis. As a result, RSF based model which took nonlinearity into account significantly outperformed traditional Cox proportional hazard model and has great potential to be a more effective approach for survival analysis.

Meta-review: adverse effects of inhaled corticosteroids relevant to older patients

BACKGROUND

In recent years, clinical trials and observational studies have raised concerns about the potential adverse effects of inhaled corticosteroids (ICS) such as pneumonia, cataract, fractures and hyperglycaemia, which are of particular concern for older patients.

METHODS

We conducted a meta-review by searching electronic databases (MEDLINE, EMBASE, PubMed) for systematic reviews and meta-analyses of ICS use and the adverse effects of interest. We also evaluated new primary studies that reported information beyond that available from previously published meta-analyses. Two reviewers independently extracted data on measures of associated harm with ICS use.

RESULTS

We identified five relevant meta-analyses for inclusion in this meta-review, and also three new studies of ICS and pneumonia. We found consistent evidence of a dose-response relationship between ICS use and serious adverse effects such as fractures and pneumonia. The estimated number needed to treat for harm due to fracture with ICS was 83 with 3-years use, and 60 per year for pneumonia. Both asthma and chronic obstructive pulmonary disease (COPD) users of ICS were at risk of pneumonia, with fluticasone appearing to confer higher risk than budesonide. There is also some suggestion that ICS use is associated with cataracts in a dose-response manner but the evidence is less robust here. Equally, the influence of ICS on diabetes mellitus remains uncertain.

CONCLUSIONS

In view of the dose-response relationship seen between ICS use and important adverse effects such as fractures and pneumonia, clinicians needs to carefully balance the benefits of ICS versus the harms in older patients receiving long-term high-dose ICS.

Simplified CRB-65 for risk stratification and predicting prognosis in acute pulmonary embolism

Background: Pulmonary embolism (PE) and community acquired pneumonia (CAP) are potentially life-threatening diseases. In CAP CRB-65 is used for risk stratification and prognosis prediction. The aim of this study was to examine a simplified CRB-65 (sCRB-65) for predicting prognosis in PE.

Methods: We retrospectively analyzed the data of 182 PE patients. Patients were, according to the score of sCRB-65 (respectively 1 point for dyspnoea, systolic blood pressure < 90 mmHg or diastolic blood pressure60 mmHg, age65years), subdivided in risk-classes 1–4.

Risk classes were compared with Kruskal-Wallis test. Logistic multivariable regression and Pearson correlation matrix were calculated for coherence of sCRB-65 and in-hospital death, right ventricular load and PE severity stadium as well as sCRB-65 > 2points and in-hospital death an PE stadium. ROC analysis was performed to evaluate efficiency of sCRB-65 score to predict in-hospital death and PE severity stadium.

Results: PE severity stadium, systolic pulmonary artery pressure (sPAP) and frequency of in-hospital death increased with growing risk class.

Risk class 1 showed lower PE sever-ity stadium than 2 (P=0.0253), 3 (P=0.0132) and 4 (P=0.00162), lower percentage of patients with sPAP > 30mmHg than 2 (0 % vs. 48.9 %, P=0.0419), 3 (0 % vs. 70.8 %, P=0.00112) and 4 (0 % vs. 75.0 %, P=0.0113). Frequency of in-hospital deaths was higher in risk class 4 than in 1 (P=0.0024), 2 (P=0.00014) and 3 (P=0.000058). Multi-variable logistic regression showed an association between sCRB-65 scored>0 and PE severity stadium (OR 11.42, 95 %CI: 1.35–96.66, P=0.0254), RVD (OR 10.09, 1.16–87.78, P=0.0363) and sPAP (OR 1.08, 1.02–1.15, P=0.0092) as well as a trend towards significance (OR 12.39, 0.90–171.34, P=0.060) between in-hospital death and sCRB-65. sCRB-65 correlated with PE severity stadium (r=0.258, P<0.001) and sPAP (r=0.280, P=0.001). sCRB-65 >2 points was strongly associated with both inhospital death (OR 36.22, 95%CI: 1.59–827.71, P=0.0245) and high-risk PE stadium (OR 57.94, 95%CI: 7.17–468.33, P=0.000141). ROC analysis for CRB-65 predicting in-hospital death and high-risk PE stadium showed AUC values of respectively 0.764 and 0.892 with sCRB-65 cut-off value of 2.5 points, respectively.

Conclusions: sCRB-65 is closely correlated with PE severity stadium and load of the right heart as well as prognosis. PE patients with sCRB-65 score >2 points revealed a 36.2-fold risk to die during in-hospital course after acute PE event. Efficiency of sCRB-65 to predict in-hospital death was good.

Development of a risk-adjusted in-hospital mortality prediction model for community-acquired pneumonia: a retrospective analysis using a Japanese administrative database

Background

Community-acquired pneumonia (CAP) is a common cause of patient hospitalization and death, and its burden on the healthcare system is increasing in aging societies. Here, we develop and internally validate risk-adjustment models and scoring systems for predicting mortality in CAP patients to enable more precise measurements of hospital performance.

Methods

Using a multicenter administrative claims database, we analyzed 35,297 patients hospitalized for CAP who had been discharged between April 1, 2012 and September 30, 2013 from 303 acute care hospitals in Japan. We developed hierarchical logistic regression models to analyze predictors of in-hospital mortality, and validated the models using the bootstrap method. Discrimination of the models was assessed using c-statistics. Additionally, we developed scoring systems based on predictors identified in the regression models.

Results

The 30-day in-hospital mortality rate was 5.8%. Predictors of in-hospital mortality included advanced age, high blood urea nitrogen level or dehydration, orientation disturbance, respiratory failure, low blood pressure, high C-reactive protein levels or high degree of pneumonic infiltration, cancer, and use of mechanical ventilation or vasopressors. Our models showed high levels of discrimination for mortality prediction, with a c-statistic of 0.89 (95% confidence interval: 0.89-0.90) in the bootstrap-corrected model. The scoring system based on 8 selected variables also showed good discrimination, with a c-statistic of 0.87 (95% confidence interval: 0.86-0.88).

Conclusions

Our mortality prediction models using administrative data showed good discriminatory power in CAP patients. These risk-adjustment models may support improvements in quality of care through accurate hospital evaluations and inter-hospital comparisons.