Prognostic Accuracy of the qSOFA Score for In-Hospital Mortality in Elderly Patients with Obstructive Acute Pyelonephritis: A Multi-Institutional Study

Prognostic accuracy of the quick sequential organ failure assessment (qSOFA) score for mortality may be limited in elderly patients. Using our multi-institutional database, we classified obstructive acute pyelonephritis (OAPN) patients into young and elderly groups, and evaluated predictive performance of the qSOFA score for in-hospital mortality. qSOFA score ≥ 2 was an independent predictor for in-hospital mortality, as was higher age, and Charlson comorbidity index (CCI) ≥ 2. In young patients, the area under the curve (AUC) of the qSOFA score for in-hospital mortality was 0.85, whereas it was 0.61 in elderly patients. The sensitivity and specificity of qSOFA score ≥ 2 for in-hospital mortality was 80% and 80% in young patients, and 50% and 68% in elderly patients, respectively. For elderly patients, we developed the CCI-incorporated qSOFA score, which showed higher prognostic accuracy compared with the qSOFA score (AUC, 0.66 vs. 0.61, p < 0.001). Therefore, the prognostic accuracy of the qSOFA score for in-hospital mortality was high in young OAPN patients, but modest in elderly patients. Although it can work as a screening tool to determine therapeutic management in young patients, for elderly patients, the presence of comorbidities should be considered at the initial assessment.

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Mortality and Prognostic Prediction in Very Elderly Patients With Severe Pneumonia

Journal of Intensive Care Medicine ◽

10.1177/0885066619826045 ◽

2019 ◽

Vol 35 (12) ◽

pp. 1405-1410 ◽

Cited By ~ 2

Author(s):

Moon Seong Baek ◽

Sojung Park ◽

Jeong-Hee Choi ◽

Cheol-Hong Kim ◽

In Gyu Hyun

Keyword(s):

Elderly Patients ◽

Hospital Mortality ◽

Pneumonia Severity Index ◽

Nursing Home Residents ◽

Area Under The Curve ◽

Severe Pneumonia ◽

Operating Characteristics ◽

Very Elderly ◽

Pneumonia Severity ◽

Prognostic Prediction

Introduction: Although prognostic prediction scores for pneumonia such as CURB-65 score or pneumonia severity index (PSI) are widely used, there were a few studies in very elderly patients. The aim of the study was to validate prognostic prediction scores for severe pneumonia and investigate risk factors associated with in-hospital mortality of severe pneumonia in very elderly patients. Methods: During the 6-year study period (from October 2012 to May 2018), 160 patients aged 80 or older admitted to medical intensive unit were analyzed retrospectively. Pneumonia severity was evaluated using CURB-65 score, PSI, Sequential Organ Failure Assessment (SOFA) scores, A-DROP, I-ROAD, UBMo index, SOAR score, and lactate. The outcome was in-hospital mortality. Results: The median age was 85 years (interquartile range: 82-88). Nursing home residents accounted for 71 (44.4%) and in-hospital mortality was 40 (25.0%). Logistic regression showed that chronic lung, mechanical ventilation, hemodialysis, and albumin were associated with in-hospital mortality of pneumonia. Using the receiver operating characteristics curve for predicting mortality, the area under the curve in pneumonia was 0.65 for the SOFA score, 0.61 for the CURB-65 score, 0.52 for the PSI, 0.58 for the A-DROP, 0.52 for the I-ROAD, 0.54 for UBMo index, 0.59 for SOAR score, and 0.65 for lactate. Conclusion: The performances of the CURB-65 and PSI are not excellent in very elderly patients with pneumonia. Further studies are needed to improve the performance of prognostic prediction scores in elderly patients.

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Thoracic Trauma in the Oldest of the Old: An Analysis of the Nationwide Inpatient Sample

The American Surgeon ◽

10.1177/000313481708300524 ◽

2017 ◽

Vol 83 (5) ◽

pp. 491-494

Author(s):

Caleb J. Mentzer ◽

Nathaniel J. Walsh ◽

Asif Talukder ◽

Zachary Klaassen ◽

Ryan Leibrandt ◽

...

Keyword(s):

Logistic Regression ◽

Mortality Rate ◽

Elderly Patients ◽

Hospital Mortality ◽

Thoracic Trauma ◽

Nationwide Inpatient Sample ◽

Significant Morbidity ◽

Geriatric Population ◽

Nationwide Inpatient Sample Database ◽

Comorbidity Index

Thoracic trauma (TT) has the second highest mortality rate in the geriatric population. These injuries cause significant morbidity in elderly patients. Little has been done to describe the demographics and mortality of specific injuries in these patients. ICD-9 codes corresponding with thoracic trauma for patients aged >80 years were extracted from the Nationwide Inpatient Sample database from 2000 to 2010. Characteristics including gender, race, Charlson Comorbidity Index (CCI), length of stay (LOS), and in-hospital mortality (IHM) were analyzed. For females and males, mean CCI was 4.84 and 4.93, respectively (P < 0.0001), and IHM was 5.49 and 2.44 per cent, respectively (P < 0.0001). For white and non-white patients, mean CCI was 4.88 and 4.84, respectively (P < 0.05), and IHM was 3.5 and 3.19 per cent, respectively. This difference was not statistically significant (P = 0.149). Logistic regression revealed correlation coefficient between CCI and mortality was 0.314 (P < 0.0001). Fitting a regression of CCI on LOS adjusting for gender and race, the adjusted effect was 0.146 (P < 0.0001). LOS was significantly less for patients surviving hospitalization. Males had higher CCI and mortality than females. Although whites had a higher CCI than non-whites, there was no difference in IHM between these two groups.

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Performance of Charlson and Elixhauser Comorbidity Index to Predict in-Hospital Mortality in Patients with Stroke in Sumadija and Western Serbia

Iranian Journal of Public Health ◽

10.18502/ijph.v50i5.6114 ◽

2021 ◽

Author(s):

Biljana Bajic ◽

Igor Galic ◽

Natasa Mihailovic ◽

Svetlana Ristic ◽

Svetlana Radevic ◽

...

Keyword(s):

Hospital Mortality ◽

Area Under The Curve ◽

Scoring Systems ◽

International Classification Of Diseases ◽

Administrative Databases ◽

Stroke Patients ◽

Discriminative Performance ◽

Increased Risk ◽

Classification Of Diseases ◽

Comorbidity Index

Background: Comorbidities are major predictors of in-hospital mortality in stroke patients. The Charlson comorbidity index (CCI) and the Elikhauser comorbidity index (ECI) are scoring systems for classifying comorbidities. We aimed to compare the performance of the CCI and ECI to predict in-hospital mortality in stroke patients. Methods: We included patients hospitalized for stroke in the Clinical Center of Kragujevac, Serbia for the last 7 years. Hospitalizations caused by stroke, were identified by the International Classification of Diseases-10 (ICD-10) codes I60.0 - I69.9. All patients were divided into two cohorts: Alive cohort (n=3297) and Mortality cohort (n=978). Results: There were significant associations between higher CCIS and increased risk of in-hospital mortality (HR = 1.07, 95% CI = 1.01–1.12) and between higher ECIS and increased risk of in-hospital mortality (HR = 1.04, 95% CI = 0.99–1.09). Almost 2/3 patients (66.9%) had comorbidities included in the CCI score and 1/3 patients (30.2%) had comorbidities included in the ECI score. The statistically significant higher CCI score (t = -3.88, df = 1017.96, P <0.01) and ECI score (t = -6.7, df = 1447.32, P <0.01) was in the mortality cohort. Area Under the Curve for ECI score was 0.606 and for CCI score was 0.549. Conclusion: Both, the CCI and the ECI can be used as scoring systems for classifying comorbidities in the administrative databases, but the model’s ECI Score had a better discriminative performance of in-hospital mortality in the stroke patients than the CCI Score model.

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Prognostic accuracy of qSOFA score for in-hospital mortality among influenza patients in the emergency department

10.21203/rs.3.rs-17308/v1 ◽

2020 ◽

Author(s):

Sheng-En Chu ◽

Chen-June Seak ◽

Tse‐Hsuan Su ◽

Chung-Hsien Chaou ◽

Hsiao-Jung Tseng ◽

...

Keyword(s):

Emergency Department ◽

Hospital Mortality ◽

Influenza Pandemic ◽

Characteristic Curve ◽

Poor Performance ◽

Rapid Screening ◽

Public Health Issue ◽

Research Database ◽

Prognostic Accuracy ◽

Qsofa Score

Abstract Background The annual seasonal influenza pandemic is an important public health issue around the world. Early prediction of patients with potentially worse outcome is important in the emergency department (ED). However, a simple and accurate predictor is yet to be developed. In this study, we aimed to investigate the effectiveness of the quick Sequential Organ Failure Assessment (qSOFA) score as a prognostic predictor of patients with influenza in the ED. Methods This is a single-center, retrospective cohort study. All the data were retrieved from a hospital-based research database. Adult patients (age ≥ 18 at admission) with a positive influenza rapid screening test or a positive influenza virus polymerase chain reaction (PCR) from 2010 to 2016 were enrolled for data analysis. qSOFA score and Systemic Inflammatory Response Syndrome (SIRS) in the ED were both collected. The primary outcome was the utility of each score in predicting in-hospital mortality. ResultsIn the study period, 3,561 patients met the inclusion criteria. The overall in-hospital mortality was 2.7% (95 patients). When the qSOFA score was 0, 1, 2, and 3, the percentage of in-hospital mortality was 0.6%, 7.2%, 15.9%, and 25%, respectively. Accordingly, the odds ratios were 7.72, 11.92, and 22.46, respectively. The sensitivity and specificity when qSOFA score ≥ 2 was 24% and 96.2%, respectively. The area under depicted receiver operating characteristic curve (AUC) was 0.864, which is significantly higher than with SIRS criteria, where the AUC was 0.786 (p < 0.01). Conclusions The qSOFA score is a useful prognostic tool for influenza and can be applied in the ED. However, it might not be a good screening triage tool because of poor sensitivity to detect high-risk patients. The SIRS score had poor performance in influenza to predict outcomes. Further studies should be performed to define its role in influenza.

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Added value of delayed computed tomography angiography in primary intracranial hemorrhage and hematoma size for predicting spot sign

Acta Radiologica ◽

10.1177/0284185117718401 ◽

2017 ◽

Vol 59 (4) ◽

pp. 485-490 ◽

Cited By ~ 2

Author(s):

Te Chang Wu ◽

Tai Yuan Chen ◽

Yow Ling Shiue ◽

Jeon Hor Chen ◽

Tsyh-Jyi Hsieh ◽

...

Keyword(s):

Computed Tomography ◽

Hospital Mortality ◽

Clinical Outcomes ◽

Computed Tomography Angiography ◽

Area Under The Curve ◽

Predictive Performance ◽

Added Value ◽

Hematoma Expansion ◽

Contrast Extravasation ◽

Spot Sign

Background The computed tomography angiography (CTA) spot sign represents active contrast extravasation within acute primary intracerebral hemorrhage (ICH) and is an independent predictor of hematoma expansion (HE) and poor clinical outcomes. The spot sign could be detected on first-pass CTA (fpCTA) or delayed CTA (dCTA). Purpose To investigate the additional benefits of dCTA spot sign in primary ICH and hematoma size for predicting spot sign. Material and Methods This is a retrospective study of 100 patients who underwent non-contrast CT (NCCT) and CTA within 24 h of onset of primary ICH. The presence of spot sign on fpCTA or dCTA, and hematoma size on NCCT were recorded. The spot sign on fpCTA or dCTA for predicting significant HE, in-hospital mortality, and poor clinical outcomes (mRS ≥ 4) are calculated. The hematoma size for prediction of CTA spot sign was also analyzed. Results Only the spot sign on dCTA could predict high risk of significant HE and poor clinical outcomes as on fpCTA ( P < 0.05). With dCTA, there is increased sensitivity and negative predictive value (NPV) for predicting significant HE, in-hospital mortality, and poor clinical outcomes. The XY value (product of the two maximum perpendicular axial dimensions) is the best predictor (area under the curve [AUC] = 0.82) for predicting spot sign on fpCTA or dCTA in the absence of intraventricular and subarachnoid hemorrhage. Conclusion This study clarifies that dCTA imaging could improve predictive performance of CTA in primary ICH. Furthermore, the XY value is the best predictor for CTA spot sign.

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A nomogram for predicting the in-hospital mortality after large hemispheric infarction

BMC Neurology ◽

10.1186/s12883-019-1571-4 ◽

2019 ◽

Vol 19 (1) ◽

Author(s):

Wenzhe Sun ◽

Guo Li ◽

Ziqiang Liu ◽

Jinfeng Miao ◽

Zhaoxia Yang ◽

...

Keyword(s):

Prognostic Factors ◽

Hospital Mortality ◽

Multivariate Logistic Regression Analysis ◽

Area Under The Curve ◽

Predictive Performance ◽

Severe Form ◽

White Blood Count ◽

Hospital Death ◽

Decompressive Hemicraniectomy ◽

Hemispheric Infarction

Abstract Background Large hemispheric infarction (LHI) is a severe form of stroke with high mortality and disability rates. The purpose of this study was to explore predictive indicators of the in-hospital mortality of LHI patients treated conservatively without decompressive hemicraniectomy. Method We performed a retrospective study of 187 consecutive patients with LHI between January 1, 2016 to May 31, 2019. The receiver operating curves were preformed to evaluate predictive performance of demographics factors, biomarkers and radiologic characteristics. Significant prognostic factors were combined to build a nomogram to predict the risk of in-hospital death of individual patients. Result One hundred fifty-eight patients with LHI were finally enrolled, 58 of which died. Through multivariate logistic regression analysis, we identified that independent prognostic factors for in-hospital death were age (adjusted odds ratio [aOR] = 1.066; 95% confidence interval [CI], 1.025–1.108; P = 0.001), midline shift (MLS, aOR = 1.330, 95% CI, 1.177–1.503; P < 0.001), and neutrophil-to-lymphocyte ratio (NLR, aOR = 3.319, 95% CI, 1.542–7.144; P = 0.002). NLR may serve as a better predictor than white blood count (WBC) and neutrophil counts. Lastly, we used all of the clinical characteristics to establish a nomogram for predicting the prognosis, area under the curve (AUC) of this nomogram was 0.858 (95% CI, 0.794–0.908). Conclusion This study shows that age, MLS, and admission NLR value are independent predictors of in-hospital mortality in patients with LHI. Moreover, nomogram, serve as a precise and convenient tool for the prognosis of LHI patients.

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P3612Validation of Charlson Comorbidity Index to predict adverse events in elderly patients with Atrial Fibrillation and Acute Coronary Syndrome: an analysis from LONGEVO-SCA Registry

European Heart Journal ◽

10.1093/eurheartj/ehz745.0471 ◽

2019 ◽

Vol 40 (Supplement_1) ◽

Author(s):

M A Esteve Pastor ◽

E Marin ◽

O Alegre ◽

J C Castillo Dominguez ◽

F Formiga ◽

...

Keyword(s):

Atrial Fibrillation ◽

Acute Coronary Syndrome ◽

Adverse Events ◽

Elderly Patients ◽

Predictive Performance ◽

Coronary Syndrome ◽

C Statistic ◽

All Cause Mortality ◽

Comorbidity Index

Abstract Background Aging is frequently characterized by the coexistence of several comorbid conditions that increase the adverse prognosis during hospitalization. There are few scores to analyze the impact of comorbidities in prognosis. Charlson Comorbidity Index (CCI). This score evaluates the burden of comorbidity in general population but the influence within cardiac diseases is unknown. Purpose The aim of this study was to analyze the relationship of CCI in adverse outcomes at short-term follow-up in elderly patients with atrial fibrillation (AF) admitted after an acute coronary syndrome (ACS). Methods The prospective multicenter LONGEVO-SCA included unselected elderly patients hospitalized after non-STACS. In this substudy, we analyze the influence of comorbidities in elderly AF patients, comparing high quartiles of CCI (Q3-Q4: high burden of comorbidities) to low quartiles (Q1-Q2) and the predictive performance of adverse events at 6 months follow-up of CCI. Results We analyzed 531 patients (mean age 84.4±3.6 years; 322 (60.6%) male). 128 (24.1%) had AF diagnosis. 91 (71.1%) patients were classified into Q1-Q2 and 37 (28.9%) patients into Q3-Q4. We analyzed the association of clinical factors and adverse events and, after Cox multivariate regression analysis, CCI was independently associated with readmissions [HR 1.19, 95% CI (1.02–1.39); p=0.020) and all-cause mortality [HR 1.32, 95% CI (1.09–1.59); p=0.003]. Patients into Q3-Q4 had higher risk of mortality than patients into Q1-Q2 [HR 5.52, 95% CI (1.01–30.3); p=0.049]. Kaplan Meier analysis showed that AF patients into Q3-Q4 had significantly worse prognosis during the follow-up with high risk of all-cause mortality (p=0.034) and readmissions due to ACS (p=0.027). We observed good predictive performance of CCI for mortality (c-statistic 0.705; p<0.001) and modest predictive performance for readmissions (c-statistic 0.627; p<0.001). Event Free Survival according Charlson Conclusions Patients into high quartiles of CCI had higher risk of adverse events during the follow-up. CCI was an independent predictor of all-cause mortality and readmissions in elderly patients. Indeed, this is the first time to validate CCI to predict adverse events in AF patients with ACS.

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The Clinical Utility of Serial Procalcitonin and Procalcitonin Clearance in Predicting the Outcome of COVID-19 Patients

10.1101/2021.06.14.21258855 ◽

2021 ◽

Author(s):

Sara I. Taha ◽

Aalaa K. Shata ◽

Shereen A. Baioumy ◽

Shaimaa H. Fouad ◽

Mariam K. Youssef

Keyword(s):

Hospital Mortality ◽

Prospective Observational Study ◽

Operating Characteristic ◽

Area Under The Curve ◽

Predictive Performance ◽

Roc Curve Analysis ◽

Laboratory Tool ◽

Serial Measurements ◽

Better Than

Background: The pandemic of coronavirus disease 2019 (COVID‐19) represents a great threat to global health. Sensitive tests that effectively predict the disease outcome are essentially required to guide proper intervention. Objectives: To evaluate the prognostic ability of serial procalcitonin (PCT) measurement to predict the outcome of COVID-19 patients, using PCT clearance (PCT-c) as a tool to reflect its dynamic changes. Methods: A prospective observational study of inpatients diagnosed with COVID-19 at the Quarantine Hospitals of Ain-Shams University, Cairo, Egypt. During the first five days of hospitalization, serial PCT and PCT-c values were obtained and compared between survivors and non-survivors. Patients were followed up to hospital discharge or in-hospital mortality. Results: Compared to survivors, serial PCT levels of non-survivors were significantly higher (p<0.001) and progressively increased during follow-up, in contrast, PCT-c values were significantly lower (p<0.01) and progressively decreased. Receiver operating characteristic (ROC) curve analysis showed that by using the initial PCT value alone, at a cut off value of 0.80 ng/ml, the area under the curve for predicting in-hospital mortality was 0.81 with 61.1% sensitivity and 87.3% accuracy. Serial measurements showed better predictive performance and the combined prediction value was better than the single prediction by the initial PCT. Conclusions: Serial PCT measurement could be a useful laboratory tool to predict the prognosis and outcome of COVID-19 patients. Moreover, PCT-c could be a reliable tool to assess PCT progressive kinetics.

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Prognostic accuracy of SIRS criteria and qSOFA score for in-hospital mortality among influenza patients in the emergency department

10.21203/rs.3.rs-17308/v2 ◽

2020 ◽

Author(s):

Sheng-En Chu ◽

Chen-June Seak ◽

Tse‐Hsuan Su ◽

Chung-Hsien Chaou ◽

Hsiao-Jung Tseng ◽

...

Keyword(s):

Emergency Department ◽

Hospital Mortality ◽

Primary Outcome ◽

Viral Infections ◽

Characteristic Curve ◽

Predictive Performance ◽

Public Health Issue ◽

Research Database ◽

Qsofa Score ◽

Sirs Criteria

Abstract Background: The seasonal influenza epidemic is an important public health issue worldwide. Early predictive identification of patients with potentially worse outcome is important in the emergency department (ED). Similarly as with bacterial infection, influenza can cause sepsis. This study was conducted to investigate the effectiveness of the Systemic Inflammatory Response Syndrome (SIRS) criteria and the quick Sequential Organ Failure Assessment (qSOFA) score as prognostic predictors for ED patients with influenza.Methods: This single-center, retrospective cohort study investigated data that was retrieved from a hospital-based research database. Adult ED patients (age ≥18 at admission) with laboratory-proven influenza from 2010 to 2016 were included for data analysis. The initial SIRS and qSOFA scores were both collected. The primary outcome was the utility of each score in the prediction of in-hospital mortality. Results: For the study period, 3,561 patients met the study inclusion criteria. The overall in-hospital mortality was 2.7% (95 patients). When the qSOFA scores were 0, 1, 2, and 3, the percentages of in-hospital mortality were 0.6%, 7.2%, 15.9%, and 25%, respectively. Accordingly, the odds ratios (ORs) were 7.72, 11.92, and 22.46, respectively. The sensitivity and specificity was 24% and 96.2%, respectively, when the qSOFA score was ≥2. However, the SIRS criteria showed no significant associations with the primary outcome. The area under the receiver operating characteristic curve (AUC) was 0.864, which is significantly higher than that with SIRS, where the AUC was 0.786 (P < 0.01).Conclusions: The qSOFA score potentially is a useful prognostic predictor for influenza and could be applied in the ED as a risk stratification tool. However, qSOFA may not be a good screening tool for triage because of its poor sensitivity. The SIRS criteria showed poor predictive performance in influenza for mortality as an outcome. Further research is needed to determine the role of these predictive tools in influenza and in other viral infections.

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Predicting mortality in adult patients with sepsis in the emergency department by using combinations of biomarkers and clinical scoring systems: a systematic review

BMC Emergency Medicine ◽

10.1186/s12873-021-00461-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Kirby Tong-Minh ◽

Iris Welten ◽

Henrik Endeman ◽

Tjebbe Hagenaars ◽

Christian Ramakers ◽

...

Keyword(s):

Systematic Review ◽

Emergency Department ◽

Scoring System ◽

Early Stage ◽

Area Under The Curve ◽

Scoring Systems ◽

Simplified Acute Physiology Score ◽

Prognostic Accuracy ◽

Clinical Scoring ◽

Qsofa Score

Abstract Background Sepsis can be detected in an early stage in the emergency department (ED) by biomarkers and clinical scoring systems. A combination of multiple biomarkers or biomarker with clinical scoring system might result in a higher predictive value on mortality. The goal of this systematic review is to evaluate the available literature on combinations of biomarkers and clinical scoring systems on 1-month mortality in patients with sepsis in the ED. Methods We performed a systematic search using MEDLINE, EMBASE and Google Scholar. Articles were included if they evaluated at least one biomarker combined with another biomarker or clinical scoring system and reported the prognostic accuracy on 28 or 30 day mortality by area under the curve (AUC) in patients with sepsis. We did not define biomarker cut-off values in advance. Results We included 18 articles in which a total of 35 combinations of biomarkers and clinical scoring systems were studied, of which 33 unique combinations. In total, seven different clinical scoring systems and 21 different biomarkers were investigated. The combination of procalcitonin (PCT), lactate, interleukin-6 (IL-6) and Simplified Acute Physiology Score-2 (SAPS-2) resulted in the highest AUC on 1-month mortality. Conclusion The studies we found in this systematic review were too heterogeneous to conclude that a certain combination it should be used in the ED to predict 1-month mortality in patients with sepsis. Future studies should focus on clinical scoring systems which require a limited amount of clinical parameters, such as the qSOFA score in combination with a biomarker that is already routinely available in the ED.

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