A comparison of predictors for mortality and bacteraemia in patients suspected of infection

Prognostic accuracy of SOFA, MEWS, and SIRS criteria in predicting the mortality rate of patients with sepsis: A meta-analysis

BACKGROUND

In recent years, some studies classified patients with sepsis and predicted their mortality by using some evaluation scales. Several studies reported significant differences in the predictive values of several tools, and the non-uniformity of the cut-off value.

OBJECTIVE

To determine and compare the prognostic accuracy of Sequential Organ Failure Assessment (SOFA) score, Modified Early Warning Score (MEWS), and Systemic Inflammatory Response Syndrome (SIRS) criteria in predicting the mortality of patients with sepsis.

METHODS

This study comprised of systematic literature review and meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. We searched PubMed, Embase, Web of Science and Cochrane Library databases from their establishment to July 31, 2022. The research articles published in the index journals provide sufficient data (true positive, false positive, true negative, and false negative results) for patients with sepsis. The combined sensitivity and specificity of the 95% confidence interval (CI) were calculated using the bivariate random effect model (BRM). The hierarchical overall subject working characteristics (HSROC) curve was drawn to evaluate the accuracy of the overall prognosis.

RESULTS

Data of 55 088 patients from 32 studies were included in this meta-analysis. SOFA had an intermediate sensitivity of 0.73 (95% CI: 0.67-0.78) and a specificity of 0.70 (0.63-0.76). SIRS criteria had the highest sensitivity of 0.75 (0.66-0.82) and the lowest specificity of 0.40 (0.29-0.52). MEWS had the lowest sensitivity of 0.49 (0.40-0.59) and the highest specificity of 0.82 (0.78-0.86).

CONCLUSIONS

Among SOFA, MEWS, and SIRS criteria, SOFA showed moderate sensitivity and specificity for predicting mortality in patients with sepsis, the highest sensitivity of SIRS and the strongest specificity of MEWS for predicting mortality in patients with sepsis. The future research direction is to combine the relevant indicators of MEWS and SIRS to develop a measurement tool with high reliability and validity.

RELEVANCE TO CLINICAL PRACTICE

The review provides useful insights into the prognostic accuracy of different assessment tools in predicting mortality in sepsis patients, which will help clinicians choose the most appropriate tool for early identification and treatment of sepsis. The findings may also contribute to the development of more accurate and reliable prognostic models for sepsis.

Machine-Learning Model for Mortality Prediction in Patients With Community-Acquired Pneumonia: Development and Validation Study

BACKGROUND

Artificial intelligence tools and techniques such as machine learning (ML) are increasingly seen as a suitable manner in which to increase the prediction capacity of currently available clinical tools, including prognostic scores. However, studies evaluating the efficacy of ML methods in enhancing the predictive capacity of existing scores for community-acquired pneumonia (CAP) are limited. We aimed to apply and validate a causal probabilistic network (CPN) model to predict mortality in patients with CAP.

RESEARCH QUESTION

Is a CPN model able to predict mortality in patients with CAP better than the commonly used severity scores?

STUDY DESIGN AND METHODS

This was a derivation-validation retrospective study conducted in two Spanish university hospitals. The ability of a CPN designed to predict mortality in sepsis (SepsisFinder [SeF]), and adapted for CAP (SeF-ML), to predict 30-day mortality was assessed and compared with other scoring systems (Pneumonia Severity Index [PSI], Sequential Organ Failure Assessment [SOFA], quick Sequential Organ Failure Assessment [qSOFA], and CURB-65 criteria [confusion, urea, respiratory rate, BP, age ≥ 65 years]). The SeF models are proprietary software. Differences between receiver operating characteristic curves were assessed by the DeLong method for correlated receiver operating characteristic curves.

RESULTS

The derivation cohort comprised 4,531 patients, and the validation cohort consisted of 1,034 patients. In the derivation cohort, the areas under the curve (AUCs) of SeF-ML, CURB-65, SOFA, PSI, and qSOFA were 0.801, 0.759, 0.671, 0.799, and 0.642, respectively, for 30-day mortality prediction. In the validation study, the AUC of SeF-ML was 0.826, concordant with the AUC (0.801) in the derivation data (P = .51). The AUC of SeF-ML was significantly higher than those of CURB-65 (0.764; P = .03) and qSOFA (0.729, P = .005). However, it did not differ significantly from those of PSI (0.830; P = .92) and SOFA (0.771; P = .14).

INTERPRETATION

SeF-ML shows potential for improving mortality prediction among patients with CAP, using structured health data. Additional external validation studies should be conducted to support generalizability.

Bacteremia Prediction With Prognostic Scores and a Causal Probabilistic Network - A Cohort Study of Emergency Department Patients

BACKGROUND

Physicians tend to overestimate patients' pretest probability of having bacteremia. The low yield of blood cultures and contaminants is associated with significant financial cost, as well as increased length of stay and unnecessary antibiotic treatment.

OBJECTIVE

This study examined the abilities of the National Early Warning Score (NEWS), the Quick Sequential Organ Failure Assessment (qSOFA), the Modified Sequential Organ Failure Assessment (mSOFA), and two versions of the causal probabilistic network, SepsisFinder™ (SF) to predict bacteremia in adult emergency department (ED) patients.

METHODS

This cohort study included adult ED patients from a large urban, academic tertiary hospital, with blood cultures obtained within 24 h of admission between 2016 and 2017. The outcome measure was true bacteremia. NEWS, qSOFA, mSOFA, and the two versions of SF score were calculated for all patients based on the first available full set of vital signs within 2 h and laboratory values within 6 h after drawing the blood cultures. Area under the receiver operating characteristic curve (AUROC) was calculated for each scoring system.

RESULTS

The study included 3106 ED patients, of which 199 (6.4%) patients had true bacteremia. The AUROCs for prediction of bacteremia were: NEWS = 0.65, qSOFA = 0.60, SF I = 0.65, mSOFA = 0.71, and SF II = 0.80.

CONCLUSIONS

Scoring systems using only vital signs, NEWS, and SF I showed moderate abilities in predicting bacteremia, whereas qSOFA performed poorly. Scoring systems using both vital signs and laboratory values, mSOFA and especially SF II, showed good abilities in predicting bacteremia.

Diagnostic accuracy of quick SOFA score and inflammatory biomarkers for predicting community-onset bacteremia

The potential use of quick SOFA (qSOFA) score and inflammatory biomarkers as bacteremia predictors is unelucidated. Herein the aim of this study was to evaluate the diagnostic accuracy of the qSOFA score and biomarkers for predicting community-onset bacteremia. We enrolled adult outpatients with blood culture samples drawn between 2018 and 2020. Contamination, intensive care unit admission, and hemodialysis were excluded. We performed a case-control study, and analyzed 115 patients (58 with bacteremia and 57 without bacteremia). The positive likelihood ratio (LR) for bacteremia was 2.46 (95% confidence interval [CI] 0.76–9.05) for a qSOFA score ≥ 2, and 4.07 (95% CI 1.92–9.58) for tachypnea (≥ 22/min). The highest performing biomarkers were procalcitonin (area under the curve [AUC] 0.80; 95% CI 0.72–0.88), followed by presepsin (AUC 0.69; 95% CI 0.60–0.79), and C-reactive protein (AUC 0.60; 95% CI 0.49–0.70). The estimated optimal cut-off value of procalcitonin was 0.377 ng/mL, with a sensitivity of 74.1%, a specificity of 73.7%, and a positive LR of 2.82. Presepsin was 407 pg/mL, with a sensitivity of 60.3%, a specificity of 75.4%, and a positive LR of 2.46. Procalcitonin was found to be a modestly useful biomarker for predicting non-severe community-onset bacteremia. Tachypnea (≥ 22/min) itself, rather than the qSOFA score, can be a diagnostic predictor. These predictors may aid decision-making regarding the collection of blood culture samples in the emergency department and outpatient clinics.