Efficacy and accuracy of qSOFA and SOFA scores as prognostic tools for community-acquired and healthcare-associated pneumonia

Utility of pneumonia severity assessment tools for mortality prediction in healthcare-associated pneumonia: a systematic review and meta-analysis

Accurate prognostic tools for mortality in patients with healthcare-associated pneumonia (HCAP) are needed to provide appropriate medical care, but the efficacy for mortality prediction of tools like PSI, A-DROP, I-ROAD, and CURB-65, widely used for predicting mortality in community-acquired and hospital-acquired pneumonia cases, remains controversial. In this study, we conducted a systematic review and meta-analysis using PubMed, Cochrane Library (trials), and Ichushi web database (accessed on August 22, 2022). We identified articles evaluating either PSI, A-DROP, I-ROAD, or CURB-65 and the mortality outcome in patients with HCAP, and calculated the pooled sensitivities, specificities, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the summary area under the curves (AUCs) for mortality prediction. Additionally, the differences in predicting prognosis among these four assessment tools were evaluated using overall AUCs pooled from AUC values reported in included studies. Eventually, 21 articles were included and these quality assessments were evaluated by QUADAS-2. Using a cut-off value of moderate in patients with HCAP, the range of pooled sensitivity, specificity, PLR, NLR, and DOR were found to be 0.91-0.97, 0.15-0.44, 1.14-1.66, 0.18-0.33, and 3.86-9.32, respectively. Upon using a cut-off value of severe in those patients, the range of pooled sensitivity, specificity, PLR, NLR, and DOR were 0.63-0.70, 0.54-0.66, 1.50-2.03, 0.47-0.58, and 2.66-4.32, respectively. Overall AUCs were 0.70 (0.68-0.72), 0.70 (0.63-0.76), 0.68 (0.64-0.73), and 0.67 (0.63-0.71), respectively, for PSI, A-DROP, I-ROAD, and CURB-65 (p = 0.66). In conclusion, these severity assessment tools do not have enough ability to predict mortality in HCAP patients. Furthermore, there are no significant differences in predictive performance among these four severity assessment tools.

Time‑dependent ROC curve analysis to determine the predictive capacity of seven clinical scales for mortality in patients with COVID‑19: Study of a hospital cohort with very high mortality

Clinical data from hospital admissions are typically utilized to determine the prognostic capacity of Coronavirus disease 2019 (COVID-19) indices. However, as disease status and severity markers evolve over time, time-dependent receiver operating characteristic (ROC) curve analysis becomes more appropriate. The present analysis assessed predictive power for death at various time points throughout patient hospitalization. In a cohort study involving 515 hospitalized patients (General Hospital Number 1 of Mexican Social Security Institute, Colima, Mexico from February 2021 to December 2022) with COVID-19, seven severity indices [Pneumonia Severity Index (PSI) PaO2/FiO2 arterial oxygen pressure/fraction of inspired oxygen (Kirby index), the Critical Illness Risk Score (COVID-GRAM), the National Early Warning Score 2 (NEWS-2), the quick Sequential Organ Failure Assessment score (qSOFA), the Fibrosis-4 index (FIB-4) and the Viral Pneumonia Mortality Score (MuLBSTA were evaluated using time-dependent ROC curves. Clinical data were collected at admission and at 2, 4, 6 and 8 days into hospitalization. The study calculated the area under the curve (AUC), sensitivity, specificity, and predictive values for each index at these time points. Mortality was 43.9%. Throughout all time points, NEWS-2 demonstrated the highest predictive power for mortality, as indicated by its AUC values. PSI and COVID-GRAM followed, with predictive power increasing as hospitalization duration progressed. Additionally, NEWS-2 exhibited the highest sensitivity (>96% in all periods) but showed low specificity, which increased from 22.9% at admission to 58.1% by day 8. PSI displayed good predictive capacity from admission to day 6 and excellent predictive power at day 8 and its sensitivity remained >80% throughout all periods, with moderate specificity (70.6-77.3%). COVID-GRAM demonstrated good predictive capacity across all periods, with high sensitivity (84.2-87.3%) but low-to-moderate specificity (61.5-67.6%). The qSOFA index initially had poor predictive power upon admission but improved after 4 days. FIB-4 had a statistically significant predictive capacity in all periods (P=0.001), but with limited clinical value (AUC, 0.639-0.698), and with low sensitivity and specificity. MuLBSTA and IKIRBY exhibited low predictive power at admission and no power after 6 days. In conclusion, in COVID-19 patients with high mortality rates, NEWS-2 and PSI consistently exhibited predictive power for death during hospital stay, with PSI demonstrating the best balance between sensitivity and specificity.

Presepsin as a prognostic biomarker in COVID-19 patients: combining clinical scoring systems and laboratory inflammatory markers for outcome prediction

BACKGROUND

There is still limited research on the prognostic value of Presepsin as a biomarker for predicting the outcome of COVID-19 patients. Additionally, research on the combined predictive value of Presepsin with clinical scoring systems and inflammation markers for disease prognosis is lacking.

METHODS

A total of 226 COVID-19 patients admitted to Beijing Youan Hospital's emergency department from May to November 2022 were screened. Demographic information, laboratory measurements, and blood samples for Presepsin levels were collected upon admission. The predictive value of Presepsin, clinical scoring systems, and inflammation markers for 28-day mortality was analyzed.

RESULTS

A total of 190 patients were analyzed, 83 (43.7%) were mild, 61 (32.1%) were moderate, and 46 (24.2%) were severe/critically ill. 23 (12.1%) patients died within 28 days. The Presepsin levels in severe/critical patients were significantly higher compared to moderate and mild patients (p < 0.001). Presepsin showed significant predictive value for 28-day mortality in COVID-19 patients, with an area under the ROC curve of 0.828 (95% CI: 0.737-0.920). Clinical scoring systems and inflammation markers also played a significant role in predicting 28-day outcomes. After Cox regression adjustment, Presepsin, qSOFA, NEWS2, PSI, CURB-65, CRP, NLR, CAR, and LCR were identified as independent predictors of 28-day mortality in COVID-19 patients (all p-values < 0.05). Combining Presepsin with clinical scoring systems and inflammation markers further enhanced the predictive value for patient prognosis.

CONCLUSION

Presepsin is a favorable indicator for the prognosis of COVID-19 patients, and its combination with clinical scoring systems and inflammation markers improved prognostic assessment.

Aetiological agents of adult community-acquired pneumonia in Japan: systematic review and meta-analysis of published data

OBJECTIVE

Epidemiological information is essential in providing appropriate empiric antimicrobial therapy for pneumonia. This study aimed to clarify the epidemiology of community-acquired pneumonia (CAP) by conducting a systematic review of published studies in Japan.

DESIGN

Systematic review.

DATA SOURCE

PubMed and Ichushi web database (January 1970 to October 2022).

ELIGIBILITY CRITERIA

Clinical studies describing pathogenic micro-organisms in CAP written in English or Japanese, excluding studies on pneumonia other than adult CAP, investigations limited to specific pathogens and case reports.

DATA EXTRACTION AND SYNTHESIS

Patient setting (inpatient vs outpatient), number of patients, concordance with the CAP guidelines, diagnostic criteria and methods for diagnosing pneumonia pathogens as well as the numbers of each isolate. A meta-analysis of various situations was performed to measure the frequency of each aetiological agent.

RESULTS

Fifty-six studies were included and 17 095 cases of CAP were identified. Pathogens were undetectable in 44.1% (95% CI 39.7% to 48.5%). Streptococcus pneumoniae was the most common cause of CAP requiring hospitalisation or outpatient care (20.0% (95% CI 17.2% to 22.8%)), followed by Haemophilus influenzae (10.8% (95% CI 7.3% to 14.3%)) and Mycoplasma pneumoniae (7.5% (95% CI 4.6% to 10.4%)). However, when limited to CAP requiring hospitalisation, Staphylococcus aureus was the third most common at 4.9% (95% CI 3.9% to 5.8%). Pseudomonas aeruginosa was more frequent in hospitalised cases, while atypical pathogens were less common. Methicillin-resistant S. aureus accounted for 40.7% (95% CI 29.0% to 52.4%) of S. aureus cases. In studies that used PCR testing for pan-respiratory viral pathogens, human enterovirus/human rhinovirus (9.4% (95% CI 0% to 20.5%)) and several other respiratory pathogenic viruses were detected. The epidemiology varied depending on the methodology and situation.

CONCLUSION

The epidemiology of CAP varies depending on the situation, such as in the hospital versus outpatient setting. Viruses are more frequently detected by exhaustive genetic searches, resulting in a significant variation in epidemiology.

Positive Rate and Utility of Blood Culture among Nursing and Healthcare-associated Pneumonia Inpatients: A Cross-sectional Study

Objective Although blood cultures to identify the presence of bacteremia are recommended for nursing- and healthcare-associated pneumonia (NHCAP), the incidence of true bacteremia and the relationship between true bacteremia and the outcome remain unclear. Physicians can therefore sometimes be confused regarding whether or not blood cultures should be obtained for NHCAP patients. This study assessed the incidence of true bacteremia and the relationship between true bacteremia and the outcome of NHCAP in a Japanese hospital setting. Methods We retrospectively analyzed NHCAP patients hospitalized between April 2016 and March 2021. The primary outcome was the incidence of true bacteremia in blood cultures. The incidence of true bacteremia was also examined according to quick Sequential Organ Failure Assessment (qSOFA) and A-DROP scores. In addition, we compared the incidence of true bacteremia between survivors and non-survivors. Results In total, 205 patients were included in this study. Blood cultures were obtained from 150 of the 205 patients (73.2%). Positive blood cultures were detected in 26 patients (17.3%), of which only 8 cases (5.3%; 95% confidence interval, 2.3-10.2%) were considered true bacteremia. Trend analyses for the incidence of true bacteremia according to qSOFA and A-DROP scores did not show any statistically significant results (p=0.49 for qSOFA; p=0.14 for A-DROP). The proportion of true bacteremia cases did not differ significantly between survivors and non-survivors. Conclusions The incidence of true bacteremia among NHCAP patients was very low. A strategy for determining indications for obtaining blood cultures from NHCAP patients needs to be established.

Correlation between Chest Computed Tomography Score and Laboratory Biomarkers in the Risk Stratification of COVID-19 Patients Admitted to the Emergency Department

BACKGROUND

It has been reported that mid-regional proadrenomedullin (MR-proADM) could be considered a useful tool to stratify the mortality risk in COVID-19 patients upon admission to the emergency department (ED). During the COVID-19 outbreak, computed tomography (CT) scans were widely used for their excellent sensitivity in diagnosing pneumonia associated with SARS-CoV-2 infection. However, the possible role of CT score in the risk stratification of COVID-19 patients upon admission to the ED is still unclear.

AIM

The main objective of this study was to assess if the association of the CT findings alone or together with MR-proADM results could ameliorate the prediction of in-hospital mortality of COVID-19 patients at the triage. Moreover, the hypothesis that CT score and MR-proADM levels together could play a key role in predicting the correct clinical setting for these patients was also evaluated.

METHODS

Epidemiological, demographic, clinical, laboratory, and outcome data were assessed and analyzed from 265 consecutive patients admitted to the triage of the ED with a SARS-CoV-2 infection.

RESULTS AND CONCLUSIONS

The accuracy results by AUROC analysis and statistical analysis demonstrated that CT score is particularly effective, when utilized together with the MR-proADM level, in the risk stratification of COVID-19 patients admitted to the ED, thus helping the decision-making process of emergency physicians and optimizing the hospital resources.

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

Community-acquired pneumonia (CAP) is one of the main reasons of mortality and morbidity in elderly population, causing substantial clinical and economic impacts. However, clinically available score systems have been shown to demonstrate poor prediction of mortality for patients aged over 65. Especially, no existing clinical model can predict morbidity and mortality for CAP patients among different age stages. Here, we aimed to understand the impact of age variable on the establishment of assessment model and explored prognostic factors and new biomarkers in predicting mortality. We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University. We used univariate and multiple logistic regression analyses to study the prognostic factors of mortality in each age-based subgroup. The prediction accuracy of the prognostic factors was determined by the Receiver Operating Characteristic curves and the area under the curves. Combination models were established using several logistic regressions to save the predicted probabilities. Four factors with independently prognostic significance were shared among all the groups, namely Albumin, BUN, NLR and Pulse, using univariate analysis and multiple logistic regression analysis. Then we built a model with these 4 variables (as ABNP model) to predict the in-hospital mortality in all three groups. The AUC value of the ABNP model were 0.888 (95% CI 0.854-0.917, p < 0.000), 0.912 (95% CI 0.880-0.938, p < 0.000) and 0.872 (95% CI 0.833-0.905, p < 0.000) in group 1, 2 and 3, respectively. We established a predictive model for mortality based on an age variable -specific study of elderly patients with CAP, with higher AUC value than PSI, CURB-65 and qSOFA in predicting mortality in different age groups (66-75/ 76-85/ over 85 years).

Serum Krebs von den Lungen-6 is associated with in-Hospital mortality of patients with severe Community-Acquired Pneumonia: A retrospective cohort study

BACKGROUND

Currently, no ideal biomarker can accurately stratify the risk of patients with severe community-acquired pneumonia (SCAP). This study aimed to evaluate the role of serum Krebs von den Lungen-6 (sKL-6) in predicting in-hospital mortality in adults with SCAP.

METHODS

In this retrospective cohort study, 249 severe pneumonia adult patients were recruited between 6 May 2021 to 30 April 2023 in Xiangya Hospital of Central South University. The sKL-6 level within 48 h of admission was measured, and the primary outcome assessed was in-hospital mortality. Multivariable logistic regression analysis was performed to calculate adjusted odds ratios (OR) with 95% confidence intervals (CI). Survival curves were plotted and subgroup analyses were conducted, stratified by relevant covariates.

RESULTS

A total of 249 patients were included in the study,with 124 patients having normal sKL-6 levels, and 125 patients having abnormal sKL-6 levels. The overall in-hospital mortality rate was 28.9% (72 out of 249 patients). Univariate and multivariate logistic regression analysis revealed that the patients with abnormal sKL-6 levels had a higher risk of in-hospital mortality compared to those with normal sKL-6 levels, both in the total SCAP patient population (OR: 5.38, 95%CI: 2.41-12.01, P < 0.001) and the non-COVID-19 SCAP patients subgroup (OR: 8.12, 95%CI: 3.16-20.84, P < 0.001). Subgroup and interaction analyses confirmed the stability of the relationship between sKL-6 levels and in-hospital mortality(P for interaction > 0.05). Kaplan-Meier survival curves showed that patients with abnormal sKL-6 levels had a higher in-hospital mortality rate than those with normal sKL-6 levels (P < 0.05). However, the results of restricted cubic spline plots(RCS) analysis demonstrated a nonlinear association between sKL-6 levels (as a continuous variable) and in-hospital mortality in patients with SCAP. Similar results were observed in non-COVID-19 SCAP patients. Furthermore, the receiver operating characteristic curve (ROC) analysis revealed that sKL-6 had superior predictive performance compared to existing biomarkers (e.g., APACHE-II, SOFA, BUN/Cr, PCT, and D-dimer) for in-hospital mortality in non-COVID-19 SCAP patients.

CONCLUSION

sKL-6 is a practical and useful biomarker for predicting in-hospital mortality in patients with SCAP.

Peripheral blood parameters for predicting PICU admission and mechanical ventilation in pediatric inpatients with human parainfluenza virus-induced pneumonia

Human parainfluenza viruses (hPIVs)-induced pneumonia is an important cause of pediatric hospitalization, and some develop severe pneumonias requiring pediatric intensive care unit (PICU) admission and mechanical ventilation (MV). The aim of this study is to investigate the value of peripheral blood (PB) parameters available on admission in predicting the need for PICU admission and MV due to pneumonia caused by hPIVs. A total of 331 cases including 277 (83.69%) on the general ward (GW) and 54 (16.31%) on the PICU were enrolled between January 2016 and June 2021. Of 54 patients admitted to the PICU, 24 patients (7.25%) received MV, whereas 30 (9.06%) did not. For both the PICU and GW groups, infants accounted for the highest proportion while school children had the lowest. Compared with the GW group, the PICU group had significantly higher rates of premature birth, fatigue, sore throat, headache, chest pain, tachypnea, dyspnea, and underlying diseases including congenital tracheal stenosis, congenital heart disease (CHD), metabolic disorder, and neurological disorder (ND), but significant lower proportion of exclusive breastfeeding and Z-scores for weight-for-height, weight-for-age, height-for-age, and body-mass-index (BMI)-for-age (BMIZ). Higher levels of some leukocyte differential counts (LDC)-related parameters including counts of neutrophil (N), ratios of neutrophil-to-lymphocyte ratio (NLR), derived neutrophils/(leukocytes minus neutrophils) ratio (dNLR), and platelet-to-lymphocyte ratio (PLR), lower levels of some other LDC-related parameters including lymphocyte (L) and monocyte (M) counts, ratios of lymphocyte-to-monocyte ratio (LMR), lymphocyte-to-C-reactive protein ratio, and prognostic nutritional index (PNI), and lower levels of PB protein (PBP)-related parameters including red blood cell (RBC), hemoglobin, total protein (TP), and serum albumin were observed in the PB of patients in the PICU compared with those in the GW. Notably, higher PLR level and two comorbidities including CHD and ND were identified as independent risk factors for PICU admission, while lower PNI level as well as smaller numbers of RBC and L as good predictors. Low levels of TP might be a useful predictor of the need for MV. Overall, the relative contributions of LDC- and PBP-related factors for accurate identification of patients required PICU admission accounted for 53.69% and 46.31%, respectively. Thus, determination of whether a patient with hPIVs-induced pneumonia is admitted to PICU involves consideration of both the LDC- and PBP-related parameters.

Incorporation of Suppression of Tumorigenicity 2 into Random Survival Forests for Enhancing Prediction of Short-Term Prognosis in Community-ACQUIRED Pneumonia

(1) Background: Biomarker and model development can help physicians adjust the management of patients with community-acquired pneumonia (CAP) by screening for inpatients with a low probability of cure early in their admission; (2) Methods: We conducted a 30-day cohort study of newly admitted adult CAP patients over 20 years of age. Prognosis models to predict the short-term prognosis were developed using random survival forest (RSF) method; (3) Results: A total of 247 adult CAP patients were studied and 208 (84.21%) of them reached clinical stability within 30 days. The soluble form of suppression of tumorigenicity-2 (sST2) was an independent predictor of clinical stability and the addition of sST2 to the prognosis model could improve the performance of the prognosis model. The C-index of the RSF model for predicting clinical stability was 0.8342 (95% CI, 0.8086–0.8598), which is higher than 0.7181 (95% CI, 0.6933–0.7429) of CURB 65 score, 0.8025 (95% CI, 0.7776–8274) of PSI score, and 0.8214 (95% CI, 0.8080–0.8348) of cox regression. In addition, the RSF model was associated with adverse clinical events during hospitalization, ICU admissions, and short-term mortality; (4) Conclusions: The RSF model by incorporating sST2 was more accurate than traditional methods in assessing the short-term prognosis of CAP patients.

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.

MuLBSTA skorunun SARS-CoV-2 pnömonili hospitalize hastalarda kritik klinik sonuçları öngörmedeki prediktif değerinin incelenmesi

Giriş:MuLBSTA (Multilobar infiltrasyon, Lenfositopeni, Bakteriyel koenfeksiyon, Sigara öyküsü, hiperTansiyon ve Yaş> 65) skoru, viral pnömonisi olan hastaları beklenen mortaliteye göre sınıflandırmak için kullanılan bir klinik tahmin kuralıdır. Hastanede yatan Sars-Cov-2 hastalarında kötü klinik sonuçlar için MuLBSTA'nın prediktif performansını PSI, CURB-65 ve qSOFA ile karşılaştırdık. Metot:Bu çalışma 11 Mart 2020 ile 31 Mayıs 2020 tarihleri arasında üçüncü basamak bir üniversite hastanesinde yatan Sars-Cov-2'li hastalar üzerinde geriye dönük yapıldı. SARS-Cov-2 testi pozitif çıkan 900 hastadan 271'i çalışmaya dahil edildi. Tüm hastalarda 30 günlük mortalite, YBÜ ihtiyacı, mekanik ventilasyon gereksinimi ve ARDS gelişimini değerlendirmek için MuLBSTA, PSI, CURB65 ve qSOFA skoru kullanıldı. 30 günlük mortalite için prognostik faktörler de analiz edildi. Bulgular:Hastanede yatan 271 hastanın 150'si (%55.3) erkekti. Ortalama yaş 54.2 ± 15.4 yıldı. 30 günlük ölüm oranı %10,7 idi. Çalışmaya dahil edilen hastalardan; 39 hasta (%14,3) yoğun bakıma yatırıldı, 32 hasta (%11,8) mekanik ventilatör desteği aldı ve 23 hasta (%8,4) ARDS tanısı aldı. Mortaliteyi tahmin etmede MuLBSTA, PSI, CURB-65 ve qSOFA skorlarının alıcı işletim karakteristik eğrisi altında kalan alan(AUROC) değerleri sırasıyla 0.877 (%95 CI 0.832 0.914), 0.853 (%95 CI 0.806-0.893), 0.769 (95% CI 0,714-0,817) ve 0,769 (95% CI 0,715-0,818). MuLBSTA puanı, diğer tahmin puanlarına kıyasla daha yüksek bir AUROC değeri gösterdi. MuLBSTA ve PSI skorları, YBÜ ihtiyacı, mekanik ventilasyon gereksinimive ARDS gelişimi olan hastaları belirlemede CURB-65 ve qSOFA skorlarından daha iyi performans gösterdi. Sonuç:MuLBSTA skoru, hastanede yatan Sars-Cov-2 hastalarında kötü klinik sonuçları tahmin etmek için etkili bir araçtır. Kullanımını doğrulamak için daha fazla çalışmaya ihtiyaç vardır.

Predictive Value of MR-proADM in the Risk Stratification and in the Adequate Care Setting of COVID-19 Patients Assessed at the Triage of the Emergency Department

In the past two pandemic years, Emergency Departments (ED) have been overrun with COVID-19-suspicious patients. Some data on the role played by laboratory biomarkers in the early risk stratification of COVID-19 patients have been recently published. The aim of this study is to assess the potential role of the new biomarker mid-regional proadrenomedullin (MR-proADM) in stratifying the in-hospital mortality risk of COVID-19 patients at the triage. A further goal of the present study is to evaluate whether MR-proADM together with other biochemical markers could play a key role in assessing the correct care level of these patients. Data from 321 consecutive patients admitted to the triage of the ED with a COVID-19 infection were analyzed. Epidemiological; demographic; clinical; laboratory; and outcome data were assessed. All the biomarkers analyzed showed an important role in predicting mortality. In particular, an increase of MR-proADM level at ED admission was independently associated with a threefold higher risk of IMV. MR-proADM showed greater ROC curves and AUC when compared to other laboratory biomarkers for the primary endpoint such as in-hospital mortality, except for CRP. This study shows that MR-proADM seems to be particularly effective for early predicting mortality and the need of ventilation in COVID-19 patients admitted to the ED.

Comparison of Pneumonia Severity Indices, qCSI, 4C-Mortality Score and qSOFA in Predicting Mortality in Hospitalized Patients with COVID-19 Pneumonia

This is a retrospective and observational study on 1511 patients with SARS-CoV-2, who were diagnosed with COVID-19 by real-time PCR testing and hospitalized due to COVID-19 pneumonia. 1511 patients, 879 male (58.17%) and 632 female (41.83%) with a mean age of 60.1 ± 14.7 were included in the study. Survivors and non-survivors groups were statistically compared with respect to survival, discharge, ICU admission and in-hospital death. Although gender was not statistically significant different between two groups, 80 (60.15%) of the patients who died were male. Mean age was 72.8 ± 11.8 in non-survivors vs. 59.9 ± 14.7 in survivors (p < 0.001). Overall in-hospital mortality was found to be 8.8% (133/1511 cases), and overall ICU admission was 10.85% (164/1511 cases). The PSI/PORT score of the non-survivors group was higher than that of the survivors group (144.38 ± 28.64 versus 67.17 ± 25.63, p < 0.001). The PSI/PORT yielding the highest performance was the best predictor for in-hospital mortality, since it incorporates the factors as advanced age and comorbidity (AUROC 0.971; % 95 CI 0.961−0.981). The use of A-DROP may also be preferred as an easier alternative to PSI/PORT, which is a time-consuming evaluation although it is more comprehensive.

A machine learning model for predicting deterioration of COVID-19 inpatients

The COVID-19 pandemic has been spreading worldwide since December 2019, presenting an urgent threat to global health. Due to the limited understanding of disease progression and of the risk factors for the disease, it is a clinical challenge to predict which hospitalized patients will deteriorate. Moreover, several studies suggested that taking early measures for treating patients at risk of deterioration could prevent or lessen condition worsening and the need for mechanical ventilation. We developed a predictive model for early identification of patients at risk for clinical deterioration by retrospective analysis of electronic health records of COVID-19 inpatients at the two largest medical centers in Israel. Our model employs machine learning methods and uses routine clinical features such as vital signs, lab measurements, demographics, and background disease. Deterioration was defined as a high NEWS2 score adjusted to COVID-19. In the prediction of deterioration within the next 7–30 h, the model achieved an area under the ROC curve of 0.84 and an area under the precision-recall curve of 0.74. In external validation on data from a different hospital, it achieved values of 0.76 and 0.7, respectively.

A Large Gap in Patients' Characteristics and Outcomes between the Real-World and Clinical Trial Settings in Community-Acquired Pneumonia and Healthcare-Associated Pneumonia

(1) Introduction: Evidence-based medicine (EBM) is necessary to standardize treatments for infections because EBM has been established based on the results of clinical trials. Since entry criteria for clinical trials are very strict, it may cause skepticism or questions on whether the results of clinical trials reflect the real world of medical practice. (2) Methods: To examine how many patients could join any randomized clinical trials for the treatment of community-acquired pneumonia (CAP) and healthcare-associated pneumonia (HCAP). We reviewed all the pneumonia patients in our institute during 2014–2017. The patients were divided into two groups: patients who were eligible for clinical trials (participation-possible group), and those who were not (participation-impossible group). Exclusion criteria for clinical trials were set based on previous clinical trials. (3) Results: A total of 406 patients were enrolled in the present study. Fifty-seven (14%) patients were categorized into the participation-possible group, while 86% of patients belonged to the participation-impossible group. Patients in the participation-possible group had less comorbidities and more favorable outcomes than those with the participation-impossible group. As for the outcomes, there were significant differences in the 30-day and in-hospital mortality rates between the two groups. In addition, the participation-possible group showed a longer overall survival time than the participation-impossible group (p < 0.001 by Log-Rank test). (4) Conclusion: There is a difference in patients’ profile and outcomes between clinical trials and the real world. Though EBM is essential to advance medicine, we should acknowledge the facts and the limits of the clinical trials.

How Do Geriatric Scores Predict 1-Year Mortality in Elderly Patients with Suspected Pneumonia?

Background: Pneumonia has an impact on long-term mortality in elderly patients. The risk factors associated with poor long-term outcomes are understated. We aimed to assess the ability of scores that evaluate patients’ comorbidities (cumulative illness rating scale—geriatric, CIRS-G), malnutrition (mini nutritional assessment, MNA) and functionality (functional independence measure, FIM) to predict 1-year mortality in a cohort of older patients having a suspicion of pneumonia. Methods: Our prospective study included consecutive patients over 65 years old and hospitalized with a suspicion of pneumonia enrolled in a monocentric cohort from May 2015 to April 2016. Each score was analysed in univariate and multivariate models and logistic regressions were used to identify contributors to 1-year mortality. Results: 200 patients were included (51% male, mean age 83.8 ± 7.7). Their 1-year mortality rate was 30%. FIM (p < 0.01), CIRS-G (p < 0.001) and MNA (p < 0.001) were strongly associated with poorer long-term outcomes in univariate analysis. CIRS-G (p < 0.05) and MNA (p < 0.05) were significant predictors of 1-year mortality in multivariate analysis. Conclusion: Long-term prognosis of patients hospitalized for pneumonia was poor and we identified that scores assessing comorbidities and malnutrition seem to be important predictors of 1-year mortality. This should be taken into account for evaluating elderly patients’ prognosis, levels and goals of care.

Recent Topics of Pneumococcal Vaccination: Indication of Pneumococcal Vaccine for Individuals at a Risk of Pneumococcal Disease in Adults

Pneumococcal disease is one of the most common and severe vaccine-preventable diseases (VPDs). Despite the advances in antimicrobial treatment, pneumococcal disease still remains a global burden and exhibits a high mortality rate among people of all ages worldwide. The immunization program of the pneumococcal conjugate vaccine (PCV) in children has decreased pneumococcal disease incidence in several countries. However, there are several problems regarding the pneumococcal vaccine, such as indications for immunocompetent persons with underlying medical conditions with a risk of pneumococcal disease, the balance of utility and cost, i.e., cost-effectiveness, vaccine coverage rate, serotype replacement, and adverse events. Especially for individuals aged 19-64 at risk of pneumococcal disease, physicians and vaccine providers should make a rational decision whether the patients should be vaccinated or not, since there is insufficient evidence supporting it. We describe this review regarding topics and problems regarding pneumococcal vaccination from the clinician's point of view.

A literature review of severity scores for adults with influenza or community-acquired pneumonia - implications for influenza vaccines and therapeutics

Influenza vaccination and antiviral therapeutics may attenuate disease, decreasing severity of illness in vaccinated and treated persons. Standardized assessment tools, definitions of disease severity, and clinical endpoints would support characterizing the attenuating effects of influenza vaccines and antivirals. We review potential clinical parameters and endpoints that may be useful for ordinal scales evaluating attenuating effects of influenza vaccines and antivirals in hospital-based studies. In studies of influenza and community-acquired pneumonia, common physiologic parameters that predicted outcomes such as mortality, ICU admission, complications, and duration of stay included vital signs (hypotension, tachypnea, fever, hypoxia), laboratory results (blood urea nitrogen, platelets, serum sodium), and radiographic findings of infiltrates or effusions. Ordinal scales based on these parameters may be useful endpoints for evaluating attenuating effects of influenza vaccines and therapeutics. Factors such as clinical and policy relevance, reproducibility, and specificity of measurements should be considered when creating a standardized ordinal scale for assessment.

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

Background

The incidence and mortality rate of community-acquired pneumonia (CAP) in elderly patients were higher than the younger population. Different scoring systems, including The quick Sequential Organ Function Assessment (qSOFA), Combination of Confusion, Urea, Respiratory Rate, Blood Pressure, and Age ≥65 (CURB-65), Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS), were used widely for predicting mortality and ICU admission of patients with community-acquired pneumonia (CAP). This study aimed to identify the most suitable score system for better hospitalization.

Methods

We retrospectively analyzed elderly patients with CAP in Minhang Hospital, Fudan University from 1 January 2018 to 1 January 2020. We recorded information of the patients including age, gender, underlying disease, consciousness state, vital signs, physiological and laboratory variables and further calculated the qSOFA, CURB-65, MEWS, and NEWS scores. Receiver operating characteristic (ROC) curves were used to predict the mortality risk and ICU admission. Kaplan–Meier survival curves were used in survival rate.

Results

In total, 1044 patients were selected for analysis and divided into two groups, namely survivor groups (902 cases) and non-survivor groups (142 cases). Depending on ICU admission enrolled patients were classified into ICU admission (n = 102) and non-ICU admission (n = 942) groups. Mortality expressed as AUC values were 0.844 (p < 0.001), 0.868 (p < 0.001), 0.927 (p < 0.001) and 0.892 (p < 0.001) for qSOFA, CURB 65, MEWS and NEWS, respectively. There were clear differences in MEWS vs CURB-65 (p < 0.0001), MEWS vs NEWS (p < 0.001), MEWS vs qSOFA (p < 0.0001). For ICU-admission, the AUC values of qSOFA, CURB-65, MEWS and NEWS scores were 0.866 (p < 0.001), 0.854 (p < 0.001), 0.922 (p < 0.001), 0.976 (p < 0.001), respectively. There were significant differences in NEWS vs CURB-65 (p < 0.0001), NEWS vs MEWS (p < 0.001), NEWS vs qSOFA (p < 0.0001).

Conclusion

We explored the outcome prediction values of CURB65, qSOFA, MEWS and NEWS for patients aged 65-years and older with community-acquired pneumonia. We found that MEWS showed superiority over the other severity scores in predicting hospital mortality, and NEWS showed superiority over the other scores in predicting ICU admission.

The Impact of Risk Factors on Treatment Outcomes of Nosocomial Pneumonia Due to Gram-Negative Bacteria in the Intensive Care Unit

Introduction

Nosocomial pneumonia is a common infection associated with high mortality in hospitalized patients. Nosocomial pneumonia, caused by gram-negative bacteria, often occurs in the elderly and patients with co-morbid diseases.

Methods

Original research using a prospective cross-sectional design was conducted on 281 patients in an intensive care unit setting with nosocomial pneumonia between July 2015 and July 2019. For each nosocomial pneumonia case, data regarding comorbidities, risk factors, patient characteristics, Charlson comorbidity index (CCI), Systemic Inflammatory Response Syndrome (SIRS), and quick Sepsis-Related Organ Failure Assessment (qSOFA) points and treatment outcomes were collected. Data were analyzed by SPSS 22.0.

Results

Nosocomial pneumonia due to gram-negative bacteria occurred in patients with neurological disorders (34.87%), heart diseases (16.37%), chronic renal failure (7.12%), and post-surgery (10.68%). Worse outcomes attributed to nosocomial pneumonia were high at 75.8%. Mechanical ventilation, change of antibiotics, and CCI ≥ 3 and qSOFA ≥ 2 were significantly negative prognostic factors (p < 0.05) on outcomes of nosocomial pneumonia. There was no difference in treatment effects between gender, age, time of onset pneumonia, SIRS score (p > 0.05). The pathogens were significant factors that influence treatment effects, but they weren't independent risk factors for poor outcomes (p = 0.823).

Conclusions

Patients with nosocomial pneumonia hospitalized in intensive care units are usually associated with many underlying diseases, including neurological diseases. Mechanical ventilation, a change in antibiotics, CCI ≥ 3, and qSOFA ≥ 2 are also associated with a worse prognosis of nosocomial pneumonia. CCI and qSOFA might be used in predicting the outcome of nosocomial pneumonia.

Supplementary Information

The online version contains supplementary material available at 10.1007/s41030-021-00175-4.

Prognostic accuracy of Quick SOFA in older adults hospitalised with community acquired urinary tract infection

INTRODUCTION

Quick [Sepsis-related] Sequential Organ Failure Assessment (qSOFA) is a prognostic score based on sepsis-3 definition, easy to carry out, whose application has been studied in older adults with sepsis from different sources and respiratory sepsis. However, to date no study has analysed its prognostic accuracy in older adults admitted to hospital with community urinary tract infection.

METHODS

In a prospective study of 282 older adults admitted to hospital with community acquired urinary tract infection, the application of qSOFA to predict hospital mortality was analysed. The predictive capacity of qSOFA for in-hospital mortality was compared with Systemic Inflammatory Response Syndrome score (SIRS) and Sequential Organ Failure Assessment (SOFA), which require laboratory test in order to be calculated.

RESULTS

In a population with a median age of 81 years, where 51.8% were males and 10.6% had septic shock, qSOFA showed sensibility and specificity of 88.46 and 75.78% and area under the receiver operating characteristic curves (AUROC) of 0.810. AUROC for qSOFA was significantly higher than that of SIRS (AUROC 0.597, P = .005) and with no statistical differences with SOFA (AUROC 0.841, P = .635).

CONCLUSION

qSOFA showed a better predictive prognostic accuracy than SIRS and similar to SOFA in older adults admitted to hospital with community acquired urinary tract infection, having the advantage of not requiring laboratory tests.

External Validation and Recalibration of the CURB-65 and PSI for Predicting 30-Day Mortality and Critical Care Intervention in Multiethnic Patients with COVID-19

OBJECTIVES

To validate and recalibrate the CURB-65 and pneumonia severity index (PSI) in predicting 30-day mortality and critical care intervention (CCI) in a multiethnic population with COVID-19, along with evaluating both models in predicting CCI.

METHODS

Retrospective data was collected for 1181 patients admitted to the largest hospital in Qatar with COVID-19 pneumonia. The area under the curve (AUC), calibration curves, and other metrics were bootstrapped to examine the performance of the models. Variables constituting the CURB-65 and PSI scores underwent further analysis using the Least Absolute Shrinkage and Selection Operator (LASSO) along with logistic regression to develop a model predicting CCI. Complex machine learning models were built for comparative analysis.

RESULTS

The PSI performed better than CURB-65 in predicting 30-day mortality (AUC 0.83, 0.78 respectively), while CURB-65 outperformed PSI in predicting CCI (AUC 0.78, 0.70 respectively). The modified PSI/CURB-65 model (respiratory rate, oxygen saturation, hematocrit, age, sodium, and glucose) predicting CCI had excellent accuracy (AUC 0.823) and good calibration.

CONCLUSIONS

Our study recalibrated, externally validated the PSI and CURB-65 for predicting 30-day mortality and CCI, and developed a model for predicting CCI. Our tool can potentially guide clinicians in Qatar to stratify patients with COVID-19 pneumonia.

Efficacy and validity of guideline-concordant treatment according to the JRS guidelines for the managements of pneumonia in adults updated in 2017 for nursing and healthcare-associated pneumonia. A propensity-matching score analysis

INTRODUCTION

Patients with nursing and healthcare-associated pneumonia (NHCAP) commonly receive empiric antibiotic therapy according to the guideline's recommendation corresponding to the patient's deteriorated conditions. However, it is unclear whether guideline-concordant treatment (GCT) could be effective or not.

PATIENTS AND METHODS

To evaluate the efficacy and validity of GCT according to the current guideline for pneumonia, we conducted this retrospective study. NHCAP patients who were admitted to our institute between 2014 and 2017 were enrolled. Based on the initial antibiotic treatment, these patients were divided into two groups, the GCT group (n = 83) and the non-GCT group (n = 146). Propensity score matching (PSM) was used to balance the baseline characteristics and potential confounders between the two groups. After PSM, patients' characteristics, microbial profiles, and clinical outcomes were evaluated.

RESULTS

Both groups were well-balanced after PSM, and 78 patients were selected from each group. There were no differences in patients' characteristics or microbial profiles between the two groups. As for outcomes, there were no differences in 30-day, in-hospital mortality rate, duration of antibiotic treatment, or admission. The severity of pneumonia was more severe in patients with the GCT group than those with the non-GCT group. Anti-pseudomonal agents as initial treatment were more frequently seen in patients with the GCT group than those in the non-GCT group.

CONCLUSION

Unlike previous studies, GCT's recommendation for management of pneumonia by the JRS in 2017 would appear to be valid and does not increase the mortality rate.

Evaluation of four novel prognostic scores on admission for COVID-19 mortality. An experience from a Mediterranean tertiary center

AIM

To assess the performance of four novel prognostic scores on admission in predicting in-hospital mortality in patients with confirmed SARS-CoV-2 infection and compare it to NEWS2 and respiratory SOFA score.

METHODS

A total of 85 adult patients admitted to a tertiary hospital in Western Greece with positive SARS-CoV-2 PCR test, were enrolled and divided into the non-survivor (n = 10) and survivor (n = 75) groups. Receiver Operating Characteristic (ROC) analysis was conducted to determine the predictive effect of the COVID-19 Mortality Score, COVID-19 Severity Index, 4 C Mortality Score and COVID-IRS NLR. Subsequently, they were compared to the respiratory component of the SOFA score and NEWS2.

RESULTS

ROC curve analysis showed that the COVID-19 Mortality Score (score ≥4) had the highest combination of sensitivity and specificity values for predicting in-hospital mortality (Sensitivity = 0.8, Specificity = 0.853). The Area Under Curve (AUC) for predicting in hospital mortality for the COVID-19 Mortality Score, COVID-19 Severity Index, 4 C Mortality Score and COVID-IRS NLR were 0.846, 0.815, 0.789 and 0.787, respectively. Comparison between the AUC of the four novel COVID-19 scores, respiratory SOFA and NEWS2 showed no significant differences.

CONCLUSION

All four novel prognostic scores had acceptable to excellent AUC values for predicting in hospital mortality. Out of the four novel prognostic scores for patients with COVID-19, the COVID-19 mortality score showed the best results in our cohort. Its prognostic ability was superior to that of the NEWS2 and respiratory SOFA score.

Ceftriaxone versus tazobactam/piperacillin and carbapenems in the treatment of aspiration pneumonia: A propensity score matching analysis

INTRODUCTION

Aspiration pneumonia (AP) accounts for 5.0-53.2% of hospitalized pneumonia and the treatment commonly used is by broad-spectrum antibiotics to cover anaerobes. Since ceftriaxone (CTRX) could generally cover oral streptococcus and anaerobes implicated in AP, it could be a useful option in the treatment of AP, instead of piperacillin-tazobactam/(PIPC/TAZ) or Carbapenems.

PATIENTS AND METHODS

For the purpose of examining whether CTRX is as effective as broad-spectrum antibiotics for the treatment of AP, this retrospective study included consecutive community-onset patients who were admitted to our institute between 2014 and 2017. These patients were divided into two groups, a CTRX group (n = 25) and a PIPC/TAZ or carbapenems group (n = 97) based on the initial antibiotic treatment. Propensity score matching (PSM) was used to balance the potential confounders, and 23 patients were selected from each group. Patients among CTXR group received CTRX, while those among PIPC/TAZ or carbapenems group received PIPC/TAZ, or carbapenems and/or other agents.

RESULTS

Both groups were well-balanced after PSM. There were no differences in 30-day mortality, duration of hospital stay or antibiotic treatments in the between them. The medical costs were much more expensive in the PIPC/TAZ or carbapenems group than in the CTR group (35,582 v. s. 8678 Japanese yen, p < 0.001).

CONCLUSION

CTRX is one of the most useful antibiotic treatment for AP, which is not inferior to broad-spectrum antibiotic treatment. In addition, usage of CTRX in the treatment of AP is more economical than broad-spectrum antibiotic treatment, and could contribute to reduction of medical costs.

Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study

Objective

To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon sequential organ failure assessment (SOFA) for decision support for a Crisis Standards of Care team.

Materials and Methods

We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the electronic health record (EHR) by combining 5 previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index.

Results

The prospective cohort included 27 296 encounters, of which 1358 (5.0%) were positive for SARS-CoV-2, 4494 (16.5%) required intensive care unit care, 1480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85.

Discussion

Stacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction.

Conclusion

We developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.

Severity Scores in COVID-19 Pneumonia: a Multicenter, Retrospective, Cohort Study

BACKGROUND

Identification of patients on admission to hospital with coronavirus infectious disease 2019 (COVID-19) pneumonia who can develop poor outcomes has not yet been comprehensively assessed.

OBJECTIVE

To compare severity scores used for community-acquired pneumonia to identify high-risk patients with COVID-19 pneumonia.

DESIGN

PSI, CURB-65, qSOFA, and MuLBSTA, a new score for viral pneumonia, were calculated on admission to hospital to identify high-risk patients for in-hospital mortality, admission to an intensive care unit (ICU), or use of mechanical ventilation. Area under receiver operating characteristics curve (AUROC), sensitivity, and specificity for each score were determined and AUROC was compared among them.

PARTICIPANTS

Patients with COVID-19 pneumonia included in the SEMI-COVID-19 Network.

KEY RESULTS

We examined 10,238 patients with COVID-19. Mean age of patients was 66.6 years and 57.9% were males. The most common comorbidities were as follows: hypertension (49.2%), diabetes (18.8%), and chronic obstructive pulmonary disease (12.8%). Acute respiratory distress syndrome (34.7%) and acute kidney injury (13.9%) were the most common complications. In-hospital mortality was 20.9%. PSI and CURB-65 showed the highest AUROC (0.835 and 0.825, respectively). qSOFA and MuLBSTA had a lower AUROC (0.728 and 0.715, respectively). qSOFA was the most specific score (specificity 95.7%) albeit its sensitivity was only 26.2%. PSI had the highest sensitivity (84.1%) and a specificity of 72.2%.

CONCLUSIONS

PSI and CURB-65, specific severity scores for pneumonia, were better than qSOFA and MuLBSTA at predicting mortality in patients with COVID-19 pneumonia. Additionally, qSOFA, the simplest score to perform, was the most specific albeit the least sensitive.

Comparing scoring systems for prediction of mortality in patients with bloodstream infection

BACKGROUND

Blood stream infections (BSIs) are associated with significant short-term mortality. There are many different scoring systems for assessing the severity of BSI.

AIM

We studied confusion, urea, respiratory rate, blood pressure, age 65(CURB65), Confusion Respiratory Rate, Blood pressure, age 65(CRB65), quick sequential organ failure assessment (qSOFA), systemic inflammatory response syndrome (SIRS) and National Early Warning Score (NEWS) and assessed how effective they were at predicting 30-day mortality across three separate BSI cohorts.

DESIGN

A retrospective analysis was performed on three established BSI cohorts: (i) All cause BSI, (ii) Escherichia coli and (iii) Streptococcus pneumoniae.

METHODS

The performance characteristics (sensitivity, specificity, positive predictive value, negative predictive value and area under receiver operating curve [AUROC]) for the prediction of 30-day mortality were calculated for the 5 scores using clinically relevant cut-offs.

RESULTS

528 patients were included: All cause BSI-148, E. coli-191 and S. pneumoniae-189. Overall, 30-day mortality was 22%. In predicting mortality, the AUROC for CURB65 and CRB65 were superior compared with qSOFA, SIRS and NEWS in the all cause BSI (0.72, 0.70, 0.66, 0.51 and 0.53) and E. coli cohorts (0.81, 0.76, 0.73, 0.55 and 0.71). In the pneumococcal cohort, CURB65, CRB65, qSOFA and NEWS were broadly equal (0.63, 0.65, 0.66 and 0.62), but all were superior to SIRS (0.57). CURB65, CRB65 and qSOFA had considerably higher accuracy than SIRS or NEWS across all cohorts.

CONCLUSION

CURB65 was superior to other scores in predicting 30-day mortality in the E. coli and all cause BSI cohorts. Further research is required to assess the potential of broadening the application of CURB65 beyond pneumonia.

The combined score of quick SOFA and the charlson comorbidity index could be a poor prognostic indicator for in-hospital mortality among patients with necrotizing fasciitis

INTRODUCTION

While necrotizing fasciitis (NF) is a rare but rapidly progressive devastating soft tissue infectious disease showing a high in-hospital mortality rate of 20-30%, there are no evidence-based predictive tools.

PATIENTS AND METHODS

For the purpose of examining which predictive tools could correctly reflect the severity and prognosis of NF, we retrospectively reviewed all patients who were diagnosed with NF at our institute. The disease severity was evaluated by quick SOFA (qSOFA), SOFA score, SIRS score, APACHE II score, LRINEC score and the combined score of qSOFA and CCI.

RESULTS

A total of 27 patients were enrolled in this study. The median age was 68 years (range 39-96 years). With respect to the predictive values for in-hospital mortality among NF patients, the area under the ROC curve for qSOFA, SOFA score, APACHE II score, the combined score of qSOFA and CCI were 0.653 (p = 0.192), 0.588 (p = 0.12), 0.709 (p = 0.075) and 0.782 (p = 0.016) respectively. A univariate analysis showed that the combined score of qSOFA and CCI≥5 and the initial treatment failure were poor prognostic indicators for the in-hospital death among NF patients. The appropriate cut-offs of qSOFA and CCI were based on the Youden Index.

CONCLUSION

We concluded that the combined score of qSOFA and CCI could reflect the severity and prognosis of NF for in-hospital death.

The role of qSOFA score and biomarkers in assessing severity of community-acquired pneumonia in adults

Introduction: Community-acquired pneumonia (CAP) is the primary cause of severe sepsis. Severity assessment scores have been created, in order to help physicians decide the proper management of CAP. The purpose of this study was to examine the correlations between different CAP severity scores, including qSOFA, several biomarkers and their predictive value in the 30 day follow-up period, regarding adverse outcome.

Materials and methods: One hundred and thirty nine adult patients with CAP, admitted in the Teaching Hospital of Infectious Diseases, Cluj-Napoca, Romania from December 2015 to February 2017, were enrolled in this study. Pneumonia Severity Index (PSI), CURB-65, SMART-COP and the qSOFA scores were calculated at admittance. Also, C-reactive protein (CRP), procalcitonin (PCT) and albumin levels were used to determine severity.

Results: The mean PSI of all patients was 93.30±41.135 points, for CURB-65 it was 1.91±0.928 points, for SMART-COP it was 1.69±1.937 points. The mean qSOFA was 1.06±0.522 points, 21 (14.9%) were at high risk of in-hospital mortality. In the group of patients with qSOFA of ≥2, all pneumonia severity scores and all biomarkers tested were higher than those with scores <2. We found significant correlations between biomarkers and severity scores, but none regarding adverse outcome.

Conclusion: The qSOFA score is easier to use and it is able to accurately evaluate the severity of CAP, similar to other scores. Biomarkers are useful in determining the severity of the CAP. Several studies are needed to assess the prediction of these biomarkers and severity scores in pneumonia regarding adverse outcome.

Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study

Background

The SARS-CoV-2 virus has infected millions of people, overwhelming critical care resources in some regions. Many plans for rationing critical care resources during crises are based on the Sequential Organ Failure Assessment (SOFA) score. The COVID-19 pandemic created an emergent need to develop and validate a novel electronic health record (EHR)-computable tool to predict mortality.

Research Questions

To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon SOFA.

Study Design and Methods

We conducted a prospective cohort study of a regional health system with 12 hospitals in Colorado between March 2020 and July 2020. All patients >14 years old hospitalized during the study period without a do not resuscitate order were included. Patients were stratified by the diagnosis of COVID-19. From this cohort, we developed and validated a model using stacked generalization to predict mortality using data widely available in the EHR by combining five previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index.

Results

We prospectively analyzed 27,296 encounters, of which 1,358 (5.0%) were positive for SARS-CoV-2, 4,494 (16.5%) included intensive care unit (ICU)-level care, 1,480 (5.4%) included invasive mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted overall mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted overall mortality with AUROC 0.94. In the subset of patients with COVID-19, we predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85.

Interpretation

We developed and validated an accurate, in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model, that improved upon SOFA.

Take Home Points

Study Question: Can we improve upon the SOFA score for real-time mortality prediction during the COVID-19 pandemic by leveraging electronic health record (EHR) data?Results: We rapidly developed and implemented a novel yet SOFA-anchored mortality model across 12 hospitals and conducted a prospective cohort study of 27,296 adult hospitalizations, 1,358 (5.0%) of which were positive for SARS-CoV-2. The Charlson Comorbidity Index and SOFA scores predicted all-cause mortality with AUROCs of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94.Interpretation: A novel EHR-based mortality score can be rapidly implemented to better predict patient outcomes during an evolving pandemic.

Prediction value of procalcitonin combining CURB-65 for 90-day mortality in community-acquired pneumonia

Background: Due to its high mortality rate, immediate and reliable severity assessment and accurate prediction of prognosis at hospital admission is critical for the management of community-acquired pneumonia (CAP) patients.Methods: Consecutive patients with primary diagnosis of CAP and hospitalized at our hospital from January 2013 to December 2015 were screened for this retrospective study. Demographic information, clinical and laboratory examination, severity model scoring, and 90-day outcomes were studied. Area under the curve (AUC) of receiver operating characteristic curve (ROC) was analyzed to compare the predictive value of different prognostic scoring methods.Results: 2099 CAP patients with a median age of 60 (IQR 44.0-73.0) years-old were included in this study. Median length of stay was 10 days (IQR 8.0-13.0). The all-cause 90-day mortality was found in 2.19% (46/2099) of all patients. PCT was identified as an independent predictor for the prognosis of CAP patients. CURB-65 in combination with PCT outperformed other predictive methods in 90-day mortality with the optimal AUC of 0.900 and Youden's Index of 0.706.Conclusions: PCT is a good marker for the assessment of severity and 90-day mortality of CAP patients. The combination of PCT and CURB-65 was more accurate than other prognostic models in predicting 90-day mortality.

Outcome prediction value of National Early Warning Score in septic patients with community-acquired pneumonia in emergency department: A single-center retrospective cohort study

BACKGROUND

To evaluate the accuracy of National Early Warning Score (NEWS) in predicting clinical outcomes (28-day mortality, intensive care unit [ICU] admission, and mechanical ventilation use) for septic patients with community-acquired pneumonia (CAP) compared with other commonly used severity scores (CURB65, Pneumonia Severity Index [PSI], Sequential Organ Failure Assessment [SOFA], quick SOFA [qSOFA], and Mortality in Emergency Department Sepsis [MEDS]) and admission lactate level.

METHODS

Adult patients diagnosed with CAP admitted between January 2017 and May 2019 with admission SOFA ≥2 from baseline were enrolled. Demographic characteristics were collected. The primary outcome was the 28-day mortality after admission, and the secondary outcome included ICU admission and mechanical ventilation use. Outcome prediction value of parameters above was compared using receiver operating characteristics (ROC) curves. Cox regression analyses were carried out to determine the risk factors for the 28-day mortality. Kaplan-Meier survival curves were plotted and compared using optimal cut-off values of qSOFA and NEWS.

RESULTS

Among the 340 enrolled patients, 90 patients were dead after a 28-day follow-up, 62 patients were admitted to ICU, and 84 patients underwent mechanical ventilation. Among single predictors, NEWS achieved the largest area under the receiver operating characteristic (AUROC) curve in predicting the 28-day mortality (0.861), ICU admission (0.895), and use of mechanical ventilation (0.873). NEWS+lactate, similar to MEDS+lactate, outperformed other combinations of severity score and admission lactate in predicting the 28-day mortality (AUROC 0.866) and ICU admission (AUROC 0.905), while NEWS+lactate did not outperform other combinations in predicting mechanical ventilation (AUROC 0.886). Admission lactate only improved the predicting performance of CURB65 and qSOFA in predicting the 28-day mortality and ICU admission.

CONCLUSIONS

NEWS could be a valuable predictor in septic patients with CAP in emergency departments. Admission lactate did not predict well the outcomes or improve the severity scores. A qSOFA ≥2 and a NEWS ≥9 were strongly associated with the 28-day mortality, ICU admission, and mechanical ventilation of septic patients with CAP in the emergency departments.

The utility of MEWS for predicting the mortality in the elderly adults with COVID-19: a retrospective cohort study with comparison to other predictive clinical scores

Background

Older adults have been reported to be a population with high-risk of death in the COVID-19 outbreak. Rapid detection of high-risk patients is crucial to reduce mortality in this population. The aim of this study was to evaluate the prognositc accuracy of the Modified Early Warning Score (MEWS) for in-hospital mortality in older adults with COVID-19.

Methods

A retrospective cohort study was conducted in Wuhan Hankou Hospital in China from 1 January 2020 to 29 February 2020. Receiver operating characteristic (ROC) analysis was used to evaluate the predictive value of MEWS, Acute Physiology and Chronic Health Evaluation II (APACHE II), Sequential Organ Function Assessment (SOFA), quick Sequential Organ Function Assessment (qSOFA), Pneumonia Severity Index (PSI), Combination of Confusion, Urea, Respiratory Rate, Blood Pressure, and Age ≥65 (CURB-65), and the Systemic Inflammatory Response Syndrome Criteria (SIRS) for in-hospital mortality. Logistic regression models were performed to detect the high-risk older adults with COVID-19.

Results

Among the 235 patients included in this study, 37 (15.74%) died and 131 (55.74%) were male, with an average age of 70.61 years (SD 8.02). ROC analysis suggested that the capacity of MEWS in predicting in-hospital mortality was as good as the APACHE II, SOFA, PSI and qSOFA (Difference in AUROC: MEWS vs. APACHE II, -0.025 (95% CI [-0.075 to 0.026]); MEWS vs. SOFA, -0.013 (95% CI [-0.049 to 0.024]); MEWS vs. PSI, -0.015 (95% CI [-0.065 to 0.035]); MEWS vs. qSOFA, 0.024 (95% CI [-0.029 to 0.076]), all P > 0.05), but was significantly higher than SIRS and CURB-65 (Difference in AUROC: MEWS vs. SIRS, 0.218 (95% CI [0.156-0.279]); MEWS vs. CURB-65, 0.064 (95% CI [0.002-0.125]), all P < 0.05). Logistic regression models implied that the male patients (≥75 years) had higher risk of death than the other older adults (estimated coefficients: 1.16, P = 0.044). Our analysis further suggests that the cut-off points of the MEWS score for the male patients (≥75 years) subpopulation and the other elderly patients should be 2.5 and 3.5, respectively.

Conclusions

MEWS is an efficient tool for rapid assessment of elderly COVID-19 patients. MEWS has promising performance in predicting in-hospital mortality and identifying the high-risk group in elderly patients with COVID-19.

Systemic Inflammatory Response and Outcomes in Community-Acquired Pneumonia Patients Categorized According to the Smoking Habit or Presence of Chronic Obstructive Pulmonary Disease

The systemic inflammatory response (SIR) may help to predict clinical progression, treatment failure, and prognosis in community-acquired pneumonia (CAP). Exposure to tobacco smoke may affect the SIR; the role of smoking in CAP has not been consolidated. We evaluated the SIR and outcomes of hospitalized CAP patients stratified by smoking habits and the presence of COPD. This retrospective analysis was conducted at the Hospital Clinic of Barcelona. Baseline, clinical, microbiological, and laboratory variables were collected at admission, using C-reactive protein (CRP) levels as a marker of SIR. The study outcomes were pleural complications, hospital stay, non-invasive and invasive mechanical ventilation (IMV), and intensive care unit (ICU) admission. We also considered the in-hospital and 30-day mortality. Data were grouped by smoking habit (non-, former-, and current-smokers) and the presence of COPD. Current smokers were younger, had fewer comorbidities, and fewer previous pneumonia episodes. CRP levels were higher in current smokers than in other groups. Current smokers had a higher risk of pleural complications independent of CRP levels, the presence of pleuritic pain, and a higher platelet count. Current smokers more often required IMV and ICU admission. Current smokers have a greater inflammatory response and are at increased risk of pleural complications.

Efficacy of the quick sequential organ failure assessment for predicting clinical outcomes among community-acquired pneumonia patients presenting in the emergency department

Background

The study aimed to investigate the predictive value of the quick sequential organ failure assessment (qSOFA) for clinical outcomes in emergency patients with community-acquired pneumonia (CAP).

Methods

A total of 742 CAP cases from the emergency department (ED) were enrolled in this study. The scoring systems including the qSOFA, SOFA and CURB-65 (confusion, urea, respiratory rate, blood pressure and age) were used to predict the prognostic outcomes of CAP in ICU-admission, acute respiratory distress syndrome (ARDS) and 28-day mortality. According to the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, the accuracies of prediction of the scoring systems were analyzed among CAP patients.

Results

The AUC values of the qSOFA, SOFA and CURB-65 scores for ICU-admission among CAP patients were 0.712 (95%CI: 0.678–0.745, P < 0.001), 0.744 (95%CI: 0.711–0.775, P < 0.001) and 0.705 (95%CI: 0.671–0.738, P < 0.001), respectively. For ARDS, the AUC values of the qSOFA, SOFA and CURB-65 scores were 0.730 (95%CI: 0.697–0.762, P < 0.001), 0.724 (95%CI: 0.690–0.756, P < 0.001) and 0.749 (95%CI: 0.716–0.780, P < 0.001), respectively. After 28 days of follow-up, the AUC values of the qSOFA, SOFA and CURB-65 scores for 28-day mortality were 0.602 (95%CI: 0.566–0.638, P < 0.001), 0.587 (95%CI: 0.551–0.623, P < 0.001) and 0.614 (95%CI: 0.577–0.649, P < 0.001) in turn. There were no statistical differences between qSOFA and SOFA scores for predicting ICU-admission (Z = 1.482, P = 0.138), ARDS (Z = 0.321, P = 0.748) and 28-day mortality (Z = 0.573, P = 0.567). Moreover, we found no differences to predict the ICU-admission (Z = 0.370, P = 0.712), ARDS (Z = 0.900, P = 0.368) and 28-day mortality (Z = 0.768, P = 0.442) using qSOFA or CURB-65 scores.

Conclusion

qSOFA was not inferior to SOFA or CURB-65 scores in predicting the ICU-admission, ARDS and 28-day mortality of patients presenting in the ED with CAP.

Predictors of mortality of influenza virus infections in a Swiss Hospital during four influenza seasons: Role of quick sequential organ failure assessment

BACKGROUND

Influenza infections have been associated with high morbidity. The aims were to determine predictors of mortality among patients with influenza infections and to ascertain the role of quick Sequential Organ Failure Assessment (qSOFA) in predicting poor outcomes.

METHODS

All adult patients with influenza infection at the Hospital of Jura, Switzerland during four influenza seasons (2014/15 to 2017/18) were included. Cepheid Xpert Xpress Flu/RSV was used during the first three influenza seasons and Cobas Influenza A/B and RSV during the 2017/18 season.

RESULTS

Among 1684 influenza virus tests performed, 441 patients with influenza infections were included (238 for influenza A virus and 203 for B). The majority of infections were community onset (369; 83.7%). Thirty-day mortality was 6.0% (25 patients). Multivariate analysis revealed that infection due to A virus (P 0.035; OR 7.1; 95% CI 1.1-43.8), malnutrition (P < 0.001; OR 25.0; 95% CI 4.5-138.8), hospital-acquired infection (P 0.003; OR 12.2; 95% CI 2.3-65.1), respiratory insufficiency (PaO₂/FiO₂ < 300) (P < 0.001; OR 125.8; 95% CI 9.6-1648.7) and pulmonary infiltrate on X-ray (P 0.020; OR 6.0; 95% CI 1.3-27.0) were identified as predictors of mortality. qSOFA showed a very good accuracy (0.89) equivalent to other more specific and burdensome scores such as CURB-65 and Pneumonia Severity Index (PSI).

CONCLUSION

qSOFA performed similarly to specific severity scores (PSI, CURB-65) in predicting mortality. Infection by influenza A virus, respiratory insufficiency and malnutrition were associated with worse prognosis.

Serum metabolite profiles as potential biochemical markers in young adults with community-acquired pneumonia cured by moxifloxacin therapy

Despite the utilization of various biochemical markers and probability calculation algorithms based on clinical studies of community-acquired pneumonia (CAP), more specific and practical biochemical markers remain to be found for improved diagnosis and prognosis. In this study, we aimed to detect the alteration of metabolite profiles, explore the correlation between serum metabolites and inflammatory markers, and seek potential biomarkers for young adults with CAP. 13 Eligible young mild CAP patients between the ages of 18 and 30 years old with CURB65 = 0 admitted to the respiratory medical department were enrolled, along with 36 healthy participants as control. Untargeted metabolomics profiling was performed and metabolites including alcohols, amino acids, carbohydrates, fatty acids, etc. were detected. A total of 227 serum metabolites were detected. L-Alanine, 2-Hydroxybutyric acid, Methylcysteine, L-Phenylalanine, Aminoadipic acid, L-Tryptophan, Rhamnose, Palmitoleic acid, Decanoylcarnitine, 2-Hydroxy-3-methylbutyric acid and Oxoglutaric acid were found to be significantly altered, which were enriched mainly in propanoate and tryptophan metabolism, as well as antibiotic-associated pathways. Aminoadipic acid was found to be significantly correlated with CRP levels and 2-Hydroxy-3-methylbutyric acid and Palmitoleic acid with PCT levels. The top 3 metabolites of diagnostic values are 2-Hydroxybutyric acid(AUC = 0.90), Methylcysteine(AUC = 0.85), and L-Alanine(AUC = 0.84). The AUC for CRP and PCT are 0.93 and 0.91 respectively. Altered metabolites were correlated with inflammation severity and were of great diagnostic value for CAP.

Machine learning algorithm to predict mortality in patients undergoing continuous renal replacement therapy

Background

Previous scoring models such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) and the Sequential Organ Failure Assessment (SOFA) scoring systems do not adequately predict mortality of patients undergoing continuous renal replacement therapy (CRRT) for severe acute kidney injury. Accordingly, the present study applies machine learning algorithms to improve prediction accuracy for this patient subset.

Methods

We randomly divided a total of 1571 adult patients who started CRRT for acute kidney injury into training (70%, n = 1094) and test (30%, n = 477) sets. The primary output consisted of the probability of mortality during admission to the intensive care unit (ICU) or hospital. We compared the area under the receiver operating characteristic curves (AUCs) of several machine learning algorithms with that of the APACHE II, SOFA, and the new abbreviated mortality scoring system for acute kidney injury with CRRT (MOSAIC model) results.

Results

For the ICU mortality, the random forest model showed the highest AUC (0.784 [0.744–0.825]), and the artificial neural network and extreme gradient boost models demonstrated the next best results (0.776 [0.735–0.818]). The AUC of the random forest model was higher than 0.611 (0.583–0.640), 0.677 (0.651–0.703), and 0.722 (0.677–0.767), as achieved by APACHE II, SOFA, and MOSAIC, respectively. The machine learning models also predicted in-hospital mortality better than APACHE II, SOFA, and MOSAIC.

Conclusion

Machine learning algorithms increase the accuracy of mortality prediction for patients undergoing CRRT for acute kidney injury compared with previous scoring models.

Prognostic Accuracy of Early Warning Scores for Clinical Deterioration in Patients With COVID-19

Background: Early Warning Scores (EWS), including the National Early Warning Score 2 (NEWS2) and Modified NEWS (NEWS-C), have been recommended for triage decision in patients with COVID-19. However, the effectiveness of these EWS in COVID-19 has not been fully validated. The study aimed to investigate the predictive value of EWS to detect clinical deterioration in patients with COVID-19. Methods: Between February 7, 2020 and February 17, 2020, patients confirmed with COVID-19 were screened for this study. The outcomes were early deterioration of respiratory function (EDRF) and need for intensive respiratory support (IRS) during the treatment process. The EDRF was defined as changes in the respiratory component of the sequential organ failure assessment (SOFA) score at day 3 (ΔSOFA_resp = SOFA _resp at day 3-SOFA_resp on admission), in which the positive value reflects clinical deterioration. The IRS was defined as the use of high flow nasal cannula oxygen therapy, noninvasive or invasive mechanical ventilation. The performances of EWS including NEWS, NEWS 2, NEWS-C, Modified Early Warning Scores (MEWS), Hamilton Early Warning Scores (HEWS), and quick sepsis-related organ failure assessment (qSOFA) for predicting EDRF and IRS were compared using the area under the receiver operating characteristic curve (AUROC). Results: A total of 116 patients were included in this study. Of them, 27 patients (23.3%) developed EDRF and 24 patients (20.7%) required IRS. Among these EWS, NEWS-C was the most accurate scoring system for predicting EDRF [AUROC 0.79 (95% CI, 0.69-0.89)] and IRS [AUROC 0.89 (95% CI, 0.82-0.96)], while NEWS 2 had the lowest accuracy in predicting EDRF [AUROC 0.59 (95% CI, 0.46-0.720)] and IRS [AUROC 0.69 (95% CI, 0.57-0.81)]. A NEWS-C ≥ 9 had a sensitivity of 59.3% and a specificity of 85.4% for predicting EDRF. For predicting IRS, a NEWS-C ≥ 9 had a sensitivity of 75% and a specificity of 88%. Conclusions: The NEWS-C was the most accurate scoring system among common EWS to identify patients with COVID-19 at risk for EDRF and need for IRS. The NEWS-C could be recommended as an early triage tool for patients with COVID-19.