Comparison the accuracy of early warning scores with qSOFA and SIRS for predicting sepsis in the emergency department

Relationship between inflammatory factors, lactic acid levels, acute skin failure, bad mood, and sleep quality

BACKGROUND

The central link between septic shock and acute skin failure (ASF) is the inflammatory response, which occurs throughout disease progression and can lead to systemic inflammatory response syndrome. Patients often experience bad moods, sleep disorders, and other health issues. Despite recognizing these factors, no studies have examined the correlation between inflammatory factors, lactic acid levels, ASF, mood disturbances, and sleep quality in critically ill patients. We hypothesize that higher levels of inflammatory factors and lactic acid are associated with more severe ASF and poorer mood and sleep quality, which may inform clinical treatment for septic shock and ASF.

AIM

To explore the relationship between inflammatory factors, lactic acid levels, the severity of ASF, bad mood, and sleep quality.

METHODS

The retrospective study included 150 patients with septic shock from the Second Hospital of Dalian Medical University, categorized into ASF (n = 35) or non-ASF groups (n = 115). We compared the peripheral blood inflammatory factors, including tumor necrosis factor-α (TNF-α), C-reactive protein (CRP), interleukin-6 (IL-6), lactic acid levels, skin mottling score (SMS), modified early warning score (MEWS), self-rating depression scale (SDS), self-rating anxiety scale (SAS), and Pittsburgh sleep quality index (PSQI) scores. Pearson correlation analysis assessed relationships among these variables.

RESULTS

The ASF group had significantly higher levels of CRP (19.60 ± 4.10 vs 15.30 ± 2.96 mg/mL), IL-6 (298.65 ± 48.65 vs 268.66 ± 33.66 pg/L), procalcitonin, lactic acid (8.42 ± 2.32 vs 5.70 ± 1.27 mmol/L), SMS [0 (0, 1) vs 3 (2, 3)], MEWS (9.34 ± 1.92 vs 6.48 ± 1.96), SAS (61.63 ± 12.03 vs 53.71 ± 12.48), SDS (60.17 ± 12.64 vs 52.27 ± 12.64), and PSQI scores (14.23 ± 3.94 vs 8.69 ± 2.46) compared with the non-ASF group (all P < 0.001). Pearson correlation analysis revealed that IL-6, CRP, TNF-α, and lactic acid were positively correlated with SMS, MEWS, SAS, SDS, and PSQI scores (P < 0.05).

CONCLUSION

Peripheral blood levels of IL-6, CRP, TNF-α, and lactic acid correlate positively with SMS, MEWS, SAS, SDS, and PSQI in critically ill patients with ASF.

Interpretable machine learning for predicting sepsis risk in emergency triage patients

The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods in emergency triage. The goal was to enhance early sepsis screening by integrating comprehensive triage information beyond vital signs. This retrospective cohort study utilized data from the MIMIC-IV database. Two models were developed: Model 1 based on vital signs alone, and Model 2 incorporating vital signs, demographic characteristics, medical history, and chief complaints. Eight ML algorithms were employed, and model performance was evaluated using metrics such as AUC, F1 Score, and calibration curves. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) methods were used to enhance model interpretability. The study included 189,617 patients, with 5.95% diagnosed with sepsis. Model 2 consistently outperformed Model 1 across most algorithms. In Model 2, Gradient Boosting achieved the highest AUC of 0.83, followed by Extra Tree, Random Forest, and Support Vector Machine (all 0.82). The SHAP method provided more comprehensible explanations for the Gradient Boosting algorithm. Modeling with comprehensive triage information using sEMR and ML methods was more effective in predicting sepsis at triage compared to using vital signs alone. Interpretable ML enhanced model transparency and provided sepsis prediction probabilities, offering a feasible approach for early sepsis screening and aiding healthcare professionals in making informed decisions during the triage process.

Adult Code Sepsis: A Narrative Review of its Implementation and Impact

This narrative review explores the implementation and impact of sepsis code protocols, an urgent intervention strategy designed to improve clinical outcomes in patients with sepsis. We examined the degree of implementation, activation criteria, areas of implementation, personnel involved, responses after activation, goals and targets, impact on clinical indicators, and challenges in implementation. The reviewed evidence suggests that sepsis codes can significantly reduce sepsis-related mortality and enhance early administration of treatments. However, variability in activation criteria and inconsistent application present ongoing challenges. The review considers the incorporation of newer scoring systems, such as NEWS and MEWS, and the potential integration of machine learning tools for early sepsis detection. It highlights the importance of tailoring implementation to specific healthcare contexts and the value of ongoing training to optimize sepsis response. Limitations include the ongoing controversy surrounding sepsis definitions and the need for standardized, feasible quality indicators. Future research should focus on standardizing activation criteria, improving protocol adherence, and exploring emerging technologies to enhance early sepsis detection and management. Despite challenges, sepsis codes show promise in improving patient outcomes when implemented thoughtfully and consistently across healthcare settings.

Comparison of modified quick Sequential Organ Failure Assessment models as triage tools for febrile patients

OBJECTIVE

Effective triage of febrile patients in the emergency department is crucial during times of overcrowding to prioritize care and allocate resources, especially during pandemics. However, available triage tools often require laboratory data and lack accuracy. We aimed to develop a simple and accurate triage tool for febrile patients by modifying the quick Sequential Organ Failure Assessment (qSOFA) score.

METHODS

We retrospectively analyzed data from 7,303 febrile patients and created modified versions of qSOFA using factors identified through multivariable analysis. The performance of these modified qSOFAs in predicting in-hospital mortality and intensive care unit (ICU) admission was compared using the area under the receiver operating characteristic curve (AUROC).

RESULTS

Through multivariable analysis, the identified factors were age ("A" factor), male sex ("M" factor), oxygen saturation measured by pulse oximetry (SpO2; "S" factor), and lactate level ("L" factor). The AUROCs of ASqSOFA (in-hospital mortality: 0.812 [95% confidence interval, 0.789-0.835]; ICU admission: 0.794 [95% confidence interval, 0.771-0.817]) were simple and not inferior to those of other more complex models (e.g., ASMqSOFA, ASLqSOFA, and ASMLqSOFA). ASqSOFA also displayed significantly higher AUROC than other triage scales, such as the Modified Early Warning Score and Korean Triage and Acuity Scale. The optimal cutoff score of ASqSOFA for the outcome was 2, and the score for redistribution to a lower level emergency department was 0.

CONCLUSION

We demonstrated that ASqSOFA can be employed as a simple and efficient triage tool for emergency febrile patients to aid in resource distribution during overcrowding. It also may be applicable in prehospital settings for febrile patient triage.

A simple electronic medical record-based predictors of illness severity in sepsis (sepsis) score

OBJECTIVE

Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort.

DESIGN

A simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors) using a population-based retrospective cohort study.

SETTING

Internal medicine services across four academic teaching hospitals in Toronto, Canada from April 2010-March 2015 (primary cohort) and 2015-2019 (secondary cohort).

PATIENTS

We identified patients admitted with sepsis based upon receipt of antibiotics and positive cultures.

MEASUREMENTS AND MAIN RESULTS

The primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a secondary cohort (2015-2019) of hospitalized patients receiving antibiotics. Our primary cohort included 1,890 patients with a median age of 72 years (IQR: 56-83). 9% died during hospitalization, 18.6% were admitted to ICU, and mean LOS was 12.7 days (SD: 21.5). In the primary and secondary (2015-2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay > 14 days, were similar between scores, in the primary and secondary cohorts. All scores had comparable calibration for predicting mortality.

CONCLUSIONS

An EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score's simplicity, it may prove a useful tool for clinical and research applications.

Early identification of suspected serious infection among patients afebrile at initial presentation using neural network models and natural language processing: A development and external validation study in the emergency department

OBJECTIVE

To develop and externally validate models based on neural networks and natural language processing (NLP) to identify suspected serious infections in emergency department (ED) patients afebrile at initial presentation.

METHODS

This retrospective study included adults who visited the ED afebrile at initial presentation. We developed four models based on artificial neural networks to identify suspected serious infection. Patient demographics, vital signs, laboratory test results and information extracted from initial ED physician notes using term frequency-inverse document frequency were used as model variables. Models were trained and internally validated with data from one hospital and externally validated using data from a different hospital. Model discrimination was evaluated using area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs).

RESULTS

The training, internal validation, and external validation datasets comprised 150,699, 37,675, and 85,098 patients, respectively. The AUCs (95% CIs) for Models 1 (demographics + vital signs), 2 (demographics + vital signs + initial ED physician note), 3 (demographics + vital signs + laboratory tests), and 4 (demographics + vital signs + laboratory tests + initial ED physician note) in the internal validation dataset were 0.789 (0.782-0.796), 0.867 (0.862-0.872), 0.881 (0.876-0.887), and 0.911 (0.906-0.915), respectively. In the external validation dataset, the AUCs (95% CIs) of Models 1, 2, 3, and 4 were 0.824 (0.817-0.830), 0.895 (0.890-0.899), 0.879 (0.873-0.884), and 0.913 (0.909-0.917), respectively. Model 1 can be utilized immediately after ED triage, Model 2 can be utilized after the initial physician notes are recorded (median time from ED triage: 28 min), and Models 3 and 4 can be utilized after the initial laboratory tests are reported (median time from ED triage: 68 min).

CONCLUSIONS

We developed and validated models to identify suspected serious infection in the ED. Extracted information from initial ED physician notes using NLP contributed to increased model performance, permitting identification of suspected serious infection at early stages of ED visits.

Clinical decision support tools useful for identifying sepsis risk

PURPOSE

Evaluate the effectiveness of the clinical decision support tools (CDSTs), POC Advisor (POCA), and Modified Early Warning System (MEWS) in identifying sepsis risk and influencing time to treatment for inpatients, comparing their respective alert mechanisms.

METHODS

This study was conducted at two academic university medical center hospitals. Data from adult inpatients in medical-surgical and telemetry units were analyzed from January 1, 2020, to December 31, 2020. Criteria included sepsis-related ICD-10 codes, antibiotic administration, and ordered sepsis labs. Subsequent statistical analyses utilized Fisher's exact test and Wilcoxon Rank Sum test, focusing on mortality differences by age, sex, and race/ethnicity.

RESULTS

Among 744 patients, 143 sepsis events were identified, with 83% already receiving treatment upon CDST alert. Group 1 (POCA alert) showed reduced response time compared with MEWS, while Group 3 (MEWS) experienced longer time to treatment. Group 4 included sepsis events missed by both systems. Mortality differences were not significant among the groups.

CONCLUSION

While CDSTs play a role, nursing assessment and clinical judgment are crucial. This study recognized the potential for alarm fatigue due to a high number of CDST-driven alerts, while emphasizing the importance of a collaborative approach for prompt sepsis treatment and potential reduction in sepsis-related mortality.

Comparing 11 early warning scores and three shock indices in early sepsis prediction in the emergency department

BACKGROUND

This study aimed to evaluate the discriminatory performance of 11 vital sign-based early warning scores (EWSs) and three shock indices in early sepsis prediction in the emergency department (ED).

METHODS

We performed a retrospective study on consecutive adult patients with an infection over 3 months in a public ED in Hong Kong. The primary outcome was sepsis (Sepsis-3 definition) within 48 h of ED presentation. Using c-statistics and the DeLong test, we compared 11 EWSs, including the National Early Warning Score 2 (NEWS2), Modified Early Warning Score, and Worthing Physiological Scoring System (WPS), etc., and three shock indices (the shock index [SI], modified shock index [MSI], and diastolic shock index [DSI]), with Systemic Inflammatory Response Syndrome (SIRS) and quick Sequential Organ Failure Assessment (qSOFA) in predicting the primary outcome, intensive care unit admission, and mortality at different time points.

RESULTS

We analyzed 601 patients, of whom 166 (27.6%) developed sepsis. NEWS2 had the highest point estimate (area under the receiver operating characteristic curve [AUROC] 0.75, 95%CI 0.70-0.79) and was significantly better than SIRS, qSOFA, other EWSs and shock indices, except WPS, at predicting the primary outcome. However, the pooled sensitivity and specificity of NEWS2 ≥ 5 for the prediction of sepsis were 0.45 (95%CI 0.37-0.52) and 0.88 (95%CI 0.85-0.91), respectively. The discriminatory performance of all EWSs and shock indices declined when used to predict mortality at a more remote time point.

CONCLUSION

NEWS2 compared favorably with other EWSs and shock indices in early sepsis prediction but its low sensitivity at the usual cut-off point requires further modification for sepsis screening.

Head-to-head comparison of six warning scores to predict mortality and clinical impairment in COVID-19 patients in emergency department

The aim was to evaluate the ability of six risk scores (4C, CURB65, SEIMC, mCHOSEN, QuickCSI, and NEWS2) to predict the outcome of patients with COVID-19 during the sixth pandemic wave in Spain. A retrospective observational study was performed to review the electronic medical records in patients ≥ 18 years of age who consulted consecutively in an emergency department with COVID-19 diagnosis throughout 2 months during the sixth pandemic wave. Clinical-epidemiological variables, comorbidities, and their respective outcomes, such as 30-day in-hospital mortality and clinical deterioration risk (a combined outcome considering: mechanical ventilation, intensive care unit admission, and/or 30-day in-hospital mortality), were calculated. The area under the curve for each risk score was calculated, and the resulting curves were compared by the Delong test, concluding with a decision curve analysis. A total of 626 patients (median age 79 years; 49.8% female) fulfilled the inclusion criteria. Two hundred and ninety-three patients (46.8%) had two or more comorbidities. Clinical deterioration risk criteria were present in 10.1% (63 cases), with a 30-day in-hospital mortality rate of 6.2% (39 cases). Comparison of the results showed that score 4C presented the best results for both outcome variables, with areas under the curve for mortality and clinical deterioration risk of 0.931 (95% CI 0.904-0.957) and 0.871 (95% CI 0.833-0.910) (both p < 0.001). The 4C Mortality Score proved to be the best score for predicting mortality or clinical deterioration risk among patients with COVID-19 attended in the emergency department in the following 30 days.

HIRA: Heart Rate Interval based Rapid Alert score to characterize autonomic dysfunction among patients with sepsis-related acute respiratory failure (ARF)

Objective. To examine whether heart rate interval based rapid alert (HIRA) score derived from a combination model of heart rate variability (HRV) and modified early warning score (MEWS) is a surrogate for the detection of acute respiratory failure (ARF) in critically ill sepsis patients.Approach. Retrospective HRV analysis of sepsis patients admitted to Emory healthcare intensive care unit (ICU) was performed between sepsis-related ARF and sepsis controls without ARF. HRV measures such as time domain, frequency domain, and nonlinear measures were analyzed up to 24 h after patient admission, 1 h before the onset of ARF, and a random event time in the sepsis controls. Statistical significance was computed by the Wilcoxon Rank Sum test. Machine learning algorithms such as eXtreme Gradient Boosting and logistic regression were developed to validate the HIRA score model. The performance of HIRA and early warning score models were evaluated using the area under the receiver operating characteristic (AUROC).Main Results. A total of 89 (ICU) patients with sepsis were included in this retrospective cohort study, of whom 31 (34%) developed sepsis-related ARF and 58 (65%) were sepsis controls without ARF. Time-domain HRV for Electrocardiogram (ECG) Beat-to-Beat RR intervals strongly distinguished ARF patients from controls. HRV measures for nonlinear and frequency domains were significantly altered (p< 0.05) among ARF compared to controls. The HIRA score AUC: 0.93; 95% confidence interval (CI): 0.88-0.98) showed a higher predictive ability to detect ARF when compared to MEWS (AUC: 0.71; 95% CI: 0.50-0.90).Significance. HRV was significantly impaired across patients who developed ARF when compared to controls. The HIRA score uses non-invasively derived HRV and may be used to inform diagnostic and therapeutic decisions regarding the severity of sepsis and earlier identification of the need for mechanical ventilation.

The reverse shock index multiplied by the Glasgow Coma Scale score can predict the need for initial resuscitation in patients suspected of sepsis

For patients suspected of sepsis, early recognition of the need for initial resuscitation is key in management. This study evaluated the ability of a modified shock index - the reverse shock index multiplied by the Glasgow Coma Scale score (rSIG) - to predict the need for initial resuscitation in patients with sepsis. This retrospective study involved adults with infection who were admitted to a Japanese tertiary care hospital from an emergency department between January and November 2020. The rSIG, modified Early Warning Score (MEWS), quick Sequential Organ Failure Assessment (qSOFA), and original shock index (SI) values were recorded using initial vital signs. The primary outcome was the area under the receiver-operating characteristic curve (AUROC) for the composite outcome consisting of vasopressor use, mechanical ventilation, and 72-h mortality. Secondary outcomes were the AUROCs for each component of the primary outcome and 28-day mortality. As a result, the primary outcome was met by 67 of the 724 patients (9%). The AUROC was significantly higher for the rSIG than for the other tools (rSIG 0.84 [0.78 - 0.88]; MEWS 0.78 [0.71 - 0.84]; qSOFA 0.72 [0.65 - 0.79]; SI 0.80 [0.74 - 0.85]). Compared with MEWS and qSOFA, the rSIG also had a higher AUROC for vasopressor use and mechanical ventilation, but not for 72-h mortality or in-hospital mortality. The rSIG could be a simple and reliable predictor of the need for initial resuscitation in patients suspected of sepsis.

Does Prehospital Suspicion of Sepsis Shorten Time to Administration of Antibiotics in the Emergency Department? A Retrospective Study in One University Hospital

Early treatment is the mainstay of sepsis therapy. We suspected that early recognition of sepsis by prehospital healthcare providers may shorten the time for antibiotic administration in the emergency department. We retrospectively evaluated all patients above 18 years of age who were diagnosed with sepsis or severe infection in our emergency department between 2018 and 2020. We recorded the suspected diagnosis at the time of presentation, the type of referring healthcare provider, and the time until initiation of antibiotic treatment. Differences between groups were calculated using the Kruskal-Wallis rank sum test. Of the 277 patients who were diagnosed with severe infection or sepsis in the emergency department, an infection was suspected in 124 (44.8%) patients, and sepsis was suspected in 31 (11.2%) patients by referring healthcare providers. Time to initiation of antibiotic treatment was shorter in patients where sepsis or infection had been suspected prior to arrival for both patients with severe infections (p = 0.022) and sepsis (p = 0.004). Given the well-described outcome benefits of early sepsis therapy, recognition of sepsis needs to be improved. Appropriate scores should be used as part of routine patient assessment to reduce the time to antibiotic administration and improve patient outcomes.

SIRS, SOFA, qSOFA, and NEWS in the diagnosis of sepsis and prediction of adverse outcomes: a systematic review and meta-analysis

BACKGROUND

We compared Systemic Inflammatory Response Syndrome (SIRS), Sequential Organ Failure Assessment (SOFA), Quick Sepsis-related Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS) for sepsis diagnosis and adverse outcomes prediction.

METHODS

Clinical studies that used SIRS, SOFA, qSOFA, and NEWS for sepsis diagnosis and prognosis assessment were included. Data were extracted, and meta-analysis was performed for outcome measures, including sepsis diagnosis, in-hospital mortality, 7/10/14-day mortality, 28/30-day mortality, and ICU admission.

RESULTS

Fifty-seven included studies showed good overall quality. Regarding sepsis prediction, SIRS demonstrated high sensitivity (0.85) but low specificity (0.41), qSOFA showed low sensitivity (0.42) but high specificity (0.98), and NEWS exhibited high sensitivity (0.71) and specificity (0.85). For predicting in-hospital mortality, SOFA demonstrated the highest sensitivity (0.89) and specificity (0.69). In terms of predicting 7/10/14-day mortality, SIRS exhibited high sensitivity (0.87), while qSOFA had high specificity (0.75). For predicting 28/30-day mortality, SOFA showed high sensitivity (0.97) but low specificity (0.14), whereas qSOFA displayed low sensitivity (0.41) but high specificity (0.88).

CONCLUSIONS

NEWS independently demonstrates good diagnostic capability for sepsis, especially in high-income countries. SOFA emerges as the optimal choice for predicting in-hospital mortality and can be employed as a screening tool for 28/30-day mortality in low-income countries.

Prevalence of electronic screening for sepsis in National Health Service acute hospitals in England

Sepsis is a worldwide public health problem. Rapid identification is associated with improved patient outcomes-if followed by timely appropriate treatment.

OBJECTIVES

Describe digital sepsis alerts (DSAs) in use in English National Health Service (NHS) acute hospitals.

METHODS

A Freedom of Information request surveyed acute NHS Trusts on their adoption of electronic patient records (EPRs) and DSAs.

RESULTS

Of the 99 Trusts that responded, 84 had an EPR. Over 20 different EPR system providers were identified as operational in England. The most common providers were Cerner (21%). System C, Dedalus and Allscripts Sunrise were also relatively common (13%, 10% and 7%, respectively). 70% of NHS Trusts with an EPR responded that they had a DSA; most of these use the National Early Warning Score (NEWS2). There was evidence that the EPR provider was related to the DSA algorithm. We found no evidence that Trusts were using EPRs to introduce data driven algorithms or DSAs able to include, for example, pre-existing conditions that may be known to increase risk.Not all Trusts were willing or able to provide details of their EPR or the underlying algorithm.

DISCUSSION

The majority of NHS Trusts use an EPR of some kind; many use a NEWS2-based DSA in keeping with national guidelines.

CONCLUSION

Many English NHS Trusts use DSAs; even those using similar triggers vary and many recreate paper systems. Despite the proliferation of machine learning algorithms being developed to support early detection of sepsis, there is little evidence that these are being used to improve personalised sepsis detection.

Scoring Systems to Evaluate the Mortality Risk of Patients with Emphysematous Cystitis: A Retrospective Observational Study

BACKGROUND

Emphysematous cystitis (EC) is a complicated urinary tract infection (UTI) characterized by gas formation within the bladder wall and lumen. Immunocompetent people are less likely to suffer from complicated UTIs, but EC usually occurs in women with poorly controlled diabetes mellitus (DM). Other risk factors of EC include recurrent UTI, neurogenic bladder disorder, blood supply disorders, and prolonged catheterization, but DM is still the most important of all aspects. Our study investigated clinical scores in predicting clinical outcomes of patients with EC. Our analysis is unique in predicting EC clinical outcomes by using scoring system performance.

MATERIALS AND METHODS

We retrospectively collected EC patient data from the electronic clinical database of Taichung Veterans General Hospital between January 2007 and December 2020. Urinary cultures and computerized tomography confirmed EC. In addition, we investigated the demographics, clinical characteristics, and laboratory data for analysis. Finally, we used a variety of clinical scoring systems as a predictor of clinical outcomes.

RESULTS

A total of 35 patients had confirmed EC, including 11 males (31.4%) and 24 females (68.6%), with a mean age of 69.1 ± 11.4 years. Their hospital stay averaged 19.9 ± 15.5 days. The in-hospital mortality rate was 22.9%. The Mortality in Emergency Department Sepsis (MEDS) score was 5.4 ± 4.7 for survivors and 11.8 ± 5.3 for non-survivors (p = 0.005). For mortality risk prediction, the AUC of ROC was 0.819 for MEDS and 0.685 for Rapid Emergency Medicine Score (REMS). The hazard ratio of univariate and multivariate logistic regression analyses of REMS for EC patients was1.457 (p = 0.011) and 1.374 (p = 0.025), respectively.

CONCLUSION

Physicians must pay attention to high-risk patients according to clinical clues and arrange imaging studies as soon as possible to confirm the diagnosis of EC. MEDS and REMS are helpful for clinical staff in predicting the clinical outcome of EC patients. If EC patients feature higher scores of MEDS (≥12) and REMS (≥10), they will have higher mortality.

Comparison of national early warning score-2 and qSOFA in predicting the prognosis of older adults with altered mental status

BACKGROUND

Altered mental status occurs in one in four older adults, and the risk increases with age. Numerous scoring systems have been improved to predict mortality, but data are limited for these scoring systems to interpret older adult patients.

AIM

We aimed to compare qSOFA and National Early Warning Score-2 (NEWS2) scores in predicting the prognosis of older adults with altered mental status.

METHOD

We included 500 older adults with altered mental status. We noted the qSOFA and NEWS2 scores of the patients. We compared the qSOFA and NEWS2 scores for the prediction of 30-day mortality, 48-h mortality, hospitalization clinic, outcome, and hospitalization length.

RESULTS

The mean NEWS2 score was 6.4, and the mean qSOFA score was 1.3. For 30-day mortality, the sensitivity and specificity of the NEWS2 score ≥ 5 were 68.29% and 69.6%, respectively, and those of qSOFA score > 1 were 47.14% and 78.75%, respectively. AUC values for 30-day mortality prediction were 0,725 (CI: 0.683-0.763) and 0.631 (CI: 0.587-0.673). For intensive care unit hospitalization prediction, the sensitivity and specificity of the NEWS2 score ≥ 5 were 52.73% and 77.67%, respectively, and those of qSOFA score > 1 were 35.32% and 81.55%, respectively. In patients with a NEWS2 score > 10 points, the predicted 48-h mortality had a specificity of 80.6%, which was higher than the qSOFA score.

CONCLUSION

NEWS2 score can be used to predict 48-h mortality, 30-day mortality, and intensive care unit hospitalization compared with qSOFA in older adults with altered mental status.

A comparison of qSOFA, SIRS and NEWS in predicting the accuracy of mortality in patients with suspected sepsis: A meta-analysis

Objective

To identify and compare prognostic accuracy of quick Sequential Organ Failure Assessment (qSOFA) score, Systemic Inflammatory Response Syndrome (SIRS) criteria, and National Early Warning Score (NEWS) to predict mortality in patients with suspected sepsis.

Methods

This meta-analysis followed accordance with the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. We searched PubMed, EMBASE, Web of Science, and the Cochrane Library databases from establishment of the database to November 29, 2021. The pooled sensitivity and specificity with 95% CIs were calculated using a bivariate random-effects model (BRM). Hierarchical summary receiver operating characteristic (HSROC) curves were generated to assess the overall prognostic accuracy.

Results

Data of 62338 patients from 26 studies were included in this meta-analysis. qSOFA had the highest specificity and the lowest sensitivity with a specificity of 0.82 (95% CI: 0.76–0.86) and a sensitivity of 0.46 (95% CI: 0.39–0.53). SIRS had the highest sensitivity and the lowest specificity with a sensitivity of 0.82 (95% CI: 0.78–0.85) and a specificity 0.24 (95% CI: 0.19–0.29). NEWS had both an intermediate sensitivity and specificity with a sensitivity of 0.73 (95% CI: 0.63–0.81) and a specificity 0.52 (95% CI: 0.39–0.65). qSOFA showed higher overall prognostic accuracy than SIRS and NEWS by comparing HSROC curves.

Conclusions

Among qSOFA, SIRS and NEWS, qSOFA showed higher overall prognostic accuracy than SIRS and NEWS. However, no scoring system has both high sensitivity and specificity for predicting the accuracy of mortality in patients with suspected sepsis.

A systematic review of the discrimination and absolute mortality predicted by the National Early Warning Scores according to different cut-off values and prediction windows

BACKGROUND

Although early warning scores were intended to simply identify patients in need of life-saving interventions, prediction has become their commonest metric. This review examined variation in the ability of the National Early Warning Scores (NEWS) in adult patients to predict absolute mortality at different times and cut-offs values.

METHOD

Following PRISMA guidelines, all studies reporting NEWS and NEWS2 providing enough information to fulfil the review's aims were included.

RESULTS

From 121 papers identified, the average area under the Receiver Operating Characteristic curve (AUC) for mortality declined from 0.90 at 24-hours to 0.76 at 30-days. Studies with a low overall mortality had a higher AUC for 24-hour mortality, as did general ward patients compared to patients seen earlier in their treatment. 24-hour mortality increased from 1.8% for a NEWS ≥3 to 7.8% for NEWS ≥7. Although 24-hour mortality for NEWS <3 was only 0.07% these deaths accounted for 9% of all deaths within 24-hours; for NEWS <7 24-hour mortality was 0.23%, which accounted for 44% of all 24-hour deaths. Within 30-days of a NEWS recording 22% of all deaths occurred in patients with a NEWS <3, 52% in patients with a NEWS <5, and 75% in patient with a NEWS <7.

CONCLUSION

NEWS reliably identifies patients most and least likely to die within 24-hours, which is what it was designed to do. However, many patients identified to have a low risk of imminent death die within 30-days. NEWS mortality predictions beyond 24-hours are unreliable.

A comparison of different scores for diagnosis and mortality prediction of adults with sepsis in Low-and-Middlencome Countries: a systematic review and meta-analysis

BACKGROUND

Clinical scores for sepsis have been primarily developed for, and applied in High-Income Countries. This systematic review and meta-analysis examined the performance of the quick Sequential Organ Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome (SIRS), Modified Early Warning Score (MEWS), and Universal Vital Assessment (UVA) scores for diagnosis and prediction of mortality in patients with suspected infection in Low-and-Middle-Income Countries.

METHODS

PubMed, Science Direct, Web of Science, and the Cochrane Central Register of Controlled Trials databases were searched until May 18, 2021. Studies reporting the performance of at least one of the above-mentioned scores for predicting mortality in patients of 15 years of age and older with suspected infection or sepsis were eligible. The Quality Assessment of Diagnostic Accuracy Studies tool was used for risk-of-bias assessment. PRISMA guidelines were followed (PROSPERO registration: CRD42020153906). The bivariate random-effects regression model was used to pool the individual sensitivities, specificities and areas-under-the-curve (AUC).

FINDINGS

Twenty-four articles (of 5669 identified) with 27,237 patients were eligible for inclusion. qSOFA pooled sensitivity was 0·70 (95% confidence interval [CI] 0·60-0·78), specificity 0·73 (95% CI 0·67-0·79), and AUC 0·77 (95% CI 0·72-0·82). SIRS pooled sensitivity, specificity and AUC were 0·88 (95% CI 0·79 -0·93), 0·34 (95% CI 0·25-0·44), and 0·69 (95% CI 0·50-0·83), respectively. MEWS pooled sensitivity, specificity and AUC were 0·70 (95% CI 0·57 -0·81), 0·61 (95% CI 0·42-0·77), and 0·72 (95% CI 0·64-0·77), respectively. UVA pooled sensitivity, specificity and AUC were 0·49 (95% CI 0·33 -0·65), 0·91(95% CI 0·84-0·96), and 0·76 (95% CI 0·44-0·93), respectively. Significant heterogeneity was observed in the pooled analysis.

INTERPRETATION

Individual score performances ranged from poor to acceptable. Future studies should combine selected or modified elements of different scores.

FUNDING

Partially funded by the UK National Institute for Health Research (NIHR) (17/63/42).

Performance of the Simple Clinical Score (SCS) and the Rapid Emergency Medicine Score (REMS) to predict severity level and mortality rate among patients with sepsis in the emergency department

Nurses play a key role as the first line of service for patients with medical conditions and injuries in the emergency department (ED), which includes assessing patients for sepsis. The researchers evaluated tools to examine the performance of the Simple Clinical Score (SCS) and the Rapid Emergency Medicine Score (REMS) to predict sepsis severity and mortality among sepsis patients in the ED. A retrospective survey was performed, selecting participants by using a purposive sampling method, and including the medical records of all patients diagnosed with sepsis admitted to the ED at Singburi Hospital, Thailand. Data were analysed using the ROC curve and the Area Under Curve (AUC) to calculate the accuracy of each patient's mortality prediction. A total of 225 patients diagnosed with sepsis was identified, with a mortality rate of 59.11% after admission to the medical service and intensive care unit. The AUC analysis showed that the accuracy of the model generated from the REMS (88.6%) was higher than that of the SCS (76.7%). The authors also recommend that key variables identified in this research should be used to develop screening and assessment tools for sepsis in the context of the ED.

Performance of NEWS2, RETTS, clinical judgment and the Predict Sepsis screening tools with respect to identification of sepsis among ambulance patients with suspected infection: a prospective cohort study

BACKGROUND

There is little evidence of which sepsis screening tool to use in the ambulance setting. The primary aim of the current study was to compare the performance of NEWS2 (National Early Warning score 2) and RETTS (Rapid Emergency Triage and Treatment System) with respect to identification of sepsis among ambulance patients with clinically suspected infection. The secondary aim was to compare the performance of the novel Predict Sepsis screening tools with that of NEWS2, RETTS and clinical judgment.

METHODS

Prospective cohort study of 323 adult ambulance patients with clinically suspected infection, transported to hospitals in Stockholm, during 2017/2018. The sensitivity, specificity, and AUC (Area Under the receiver operating Curve) were calculated and compared by using McNemar´s test and DeLong's test.

RESULTS

The prevalence of sepsis in the current study population was 44.6% (144 of 323 patients). No significant difference in AUC was demonstrated between NEWS2 ≥ 5 and RETTS ≥ orange. NEWS2 ≥ 7 demonstrated a significantly greater AUC than RETTS red. The Predict Sepsis screening tools ≥ 2 demonstrated the highest sensitivity (range 0.87-0.91), along with RETTS ≥ orange (0.83), but the lowest specificity (range 0.39-0.49). The AUC of NEWS2 (0.73) and the Predict Sepsis screening tools (range 0.75-0.77) was similar.

CONCLUSIONS

The results indicate that NEWS2 could be the better alternative for sepsis identification in the ambulance, as compared to RETTS. The Predict Sepsis screening tools demonstrated a high sensitivity and AUCs similar to that of NEWS2. However, these results need to be interpreted with caution as the Predict Sepsis screening tools require external validation.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03249597. Registered 15 August 2017-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03249597 .

National Early Warning Score Does Not Accurately Predict Mortality for Patients With Infection Outside the Intensive Care Unit: A Systematic Review and Meta-Analysis

Background: The prognostic value of the national early warning score (NEWS) in patients with infections remains controversial. We aimed to evaluate the prognostic accuracy of NEWS for prediction of in-hospital mortality in patients with infections outside the intensive care unit (ICU). Methods: We searched PubMed, Embase, and Scopus for related articles from January 2012 to April 2021. Sensitivity, specificity, and likelihood ratios were pooled by using the bivariate random-effects model. Overall prognostic performance was summarized by using the area under the curve (AUC). We performed subgroup analyses to assess the prognostic accuracy of NEWS in selected populations. Results: A total of 21 studies with 107,008 participants were included. The pooled sensitivity and specificity of NEWS were 0.71 and 0.60. The pooled AUC of NEWS was 0.70, which was similar to quick sequential organ failure assessment (qSOFA, AUC: 0.70) and better than systemic inflammatory response syndrome (SIRS, AUC: 0.60). However, the sensitivity (0.55) and AUC (0.63) of NEWS were poor in elder patients. The NEWS of 5 was more sensitive, which was a better threshold for activating urgent assessment and treatment. Conclusions: The NEWS had good diagnostic accuracy for early prediction of mortality in patients with infections outside the ICU, and the sensitivity and specificity were more moderate when compared with qSOFA and SIRS. Insufficient sensitivity and poor performance in the elder population may have limitations as an early warning score for adverse outcomes. NEWS should be used for continuous monitoring rather than a single time point predictive tool.

Modified National Early Warning Score as Early Predictor of Outcome in COVID-19 Pandemic

COVID-19 represented an important challenge to the Italian healthcare system (IHCS). Our main aim was to obtain evidence to support the use of modified national early warning score (m-NEWS) as an interdisciplinary, common, and universal scoring scale to quickly recognize patients with a risk of clinical deterioration before admission and during hospitalization. As a secondary goal, we tried to find a score threshold that can trigger patients' immediate medical review as a part of an optimal triaging protocol for an emergency setting where healthcare resources are overloaded. We performed a retrospective observational study. We included in our study all patients treated for COVID-19 infection in surgical departments between 01 March 2020 and 16 April 2020. Patients with negative test results for SARS-COV-2 were excluded. m-NEWS was obtained twice a day. Patients' m-NEWS were analyzed in order to verify the correlation between m-NEWS (at admission and m-NEWS variation 24 h after admission) and outcome (positive outcome-survival, negative outcome-death, or intensive care unit (ICU) transfer). We included a population-based sample of 225 SARS-COV-2-infected patients. Overall, the average age at hospitalization was 71 (ranging from 40 to 95). 144 (64%) patients were males and 81 (36%) females. m-NEWS values lower or equal to 7 were associated with the majority of the "recovered" population (100/132 75.75%) and at the same time with the minority of the "non-recovered" population (25/93 26.88%). For our sample, age is statistically correlated to the outcome but a triage protocol based solely on this variable is less effective than m-NEWS, which showed to be a reliable and easy-to-use score for first patient evaluation. Our observations pave the way towards further studies aiming at optimizing territorial and community healthcare management protocols.

Clinical performance of early warning scoring systems for identifying sepsis among anti-hypertensive agent users

BACKGROUND

Little is known about the accuracy of the quick Sequential Organ Failure Assessment (qSOFA) and the National Early Warning Score (NEWS) in identifying sepsis patients with a history of hypertension on anti-hypertensive agents, which affect vital signs as components of the scoring systems. We aimed to examine the ability of qSOFA and NEWS to predict sepsis among anti-hypertensive agent users by comparing them with non-users.

METHODS

We retrospectively identified adult patients (aged ≥18years) with suspected infection who presented to an emergency department (ED) of a large tertiary medical center in Japan between April 2018 and March 2020. Suspected infection was defined based on the chief complaint of fever, high body temperature, or the clinical context on arrival at the ED. We excluded patients who had trauma or cardiac arrest, those who were transported to other hospitals after arrival at the ED, and those whose vital signs data were mostly missing. The predictive performances of qSOFA and NEWS based on initial vital signs were examined separately for sepsis, ICU admission, and in-hospital mortality and compared between anti-hypertensive agent users and non-users.

RESULTS

Among 2900 patients with suspected infection presenting to the ED, 291 (10%) had sepsis, 1023 (35%) were admitted to the ICU, and 188 (6.5%) died. The prediction performances of qSOFA and NEWS for each outcome among anti-hypertensive agent users were lower than that among non-users (e.g., c-statistics of qSOFA for sepsis, 0.66 vs. 0.71, p = 0.07; and for ICU admission, 0.70 vs. 0.75, p = 0.01). For identifying sepsis, the sensitivity and specificity of qSOFA ≥2 were 0.43 and 0.77 in anti-hypertensive agent users and 0.51 and 0.82 in non-users. Similar associations were observed for identifying ICU admission and in-hospital mortality. Regardless of the use of anti-hypertensive agents, NEWS had better prediction abilities for each outcome than qSOFA.

CONCLUSION

The clinical performance of qSOFA and NEWS for identifying sepsis among anti-hypertensive agent users was likely lower than that among non-users.