Acute Care Diagnostics Collaboration: Assessment of a Bayesian clinical decision model integrating the Prehospital Sepsis Score and point-of-care lactate

Prehospital early warning scores for adults with suspected sepsis: the PHEWS observational cohort and decision-analytic modelling study

Background

Guidelines for sepsis recommend treating those at highest risk within 1 hour. The emergency care system can only achieve this if sepsis is recognised and prioritised. Ambulance services can use prehospital early warning scores alongside paramedic diagnostic impression to prioritise patients for treatment or early assessment in the emergency department.

Objectives

To determine the accuracy, impact and cost-effectiveness of using early warning scores alongside paramedic diagnostic impression to identify sepsis requiring urgent treatment.

Design

Retrospective diagnostic cohort study and decision-analytic modelling of operational consequences and cost-effectiveness.

Setting

Two ambulance services and four acute hospitals in England.

Participants

Adults transported to hospital by emergency ambulance, excluding episodes with injury, mental health problems, cardiac arrest, direct transfer to specialist services, or no vital signs recorded.

Interventions

Twenty-one early warning scores used alongside paramedic diagnostic impression, categorised as sepsis, infection, non-specific presentation, or other specific presentation.

Main outcome measures

Proportion of cases prioritised at the four hospitals; diagnostic accuracy for the sepsis-3 definition of sepsis and receiving urgent treatment (primary reference standard); daily number of cases with and without sepsis prioritised at a large and a small hospital; the minimum treatment effect associated with prioritisation at which each strategy would be cost-effective, compared to no prioritisation, assuming willingness to pay £20,000 per quality-adjusted life-year gained.

Results

Data from 95,022 episodes involving 71,204 patients across four hospitals showed that most early warning scores operating at their pre-specified thresholds would prioritise more than 10% of cases when applied to non-specific attendances or all attendances. Data from 12,870 episodes at one hospital identified 348 (2.7%) with the primary reference standard. The National Early Warning Score, version 2 (NEWS2), had the highest area under the receiver operating characteristic curve when applied only to patients with a paramedic diagnostic impression of sepsis or infection (0.756, 95% confidence interval 0.729 to 0.783) or sepsis alone (0.655, 95% confidence interval 0.63 to 0.68). None of the strategies provided high sensitivity (> 0.8) with acceptable positive predictive value (> 0.15). NEWS2 provided combinations of sensitivity and specificity that were similar or superior to all other early warning scores. Applying NEWS2 to paramedic diagnostic impression of sepsis or infection with thresholds of > 4, > 6 and > 8 respectively provided sensitivities and positive predictive values (95% confidence interval) of 0.522 (0.469 to 0.574) and 0.216 (0.189 to 0.245), 0.447 (0.395 to 0.499) and 0.274 (0.239 to 0.313), and 0.314 (0.268 to 0.365) and 0.333 (confidence interval 0.284 to 0.386). The mortality relative risk reduction from prioritisation at which each strategy would be cost-effective exceeded 0.975 for all strategies analysed.

Limitations

We estimated accuracy using a sample of older patients at one hospital. Reliable evidence was not available to estimate the effectiveness of prioritisation in the decision-analytic modelling.

Conclusions

No strategy is ideal but using NEWS2, in patients with a paramedic diagnostic impression of infection or sepsis could identify one-third to half of sepsis cases without prioritising unmanageable numbers. No other score provided clearly superior accuracy to NEWS2. Research is needed to develop better definition, diagnosis and treatments for sepsis.

Study registration

This study is registered as Research Registry (reference: researchregistry5268).

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/136/10) and is published in full in Health Technology Assessment; Vol. 28, No. 16. See the NIHR Funding and Awards website for further award information.

Prehospital Sepsis Recognition and Outcomes for Patients with Sepsis by Race and Ethnicity

BACKGROUND

First medical contact for patients with sepsis often initiates in the prehospital setting, yet limited studies have explored the EMS sepsis recognition-mortality relationship. Racial and ethnic minority patients often have worse sepsis outcomes, yet the role of prehospital recognition in this inequity has not been explored. Our objective was to describe prehospital sepsis recognition and hospital mortality, with analysis by patient race and ethnicity.

METHODS

Using linked EMS and hospital records from the 2021 ESO Data Collaborative, we retrospectively analyzed 9-1-1 EMS transports for adult patients with emergency department ICD-10 sepsis diagnosis codes. EMS sepsis recognition was defined as a primary or secondary sepsis impression, use of an electronic health record specialty sepsis form, or a prehospital sepsis alert. We used multivariable logistic regression to assess the association between EMS sepsis recognition and hospital mortality, adjusting for age, sex, race and ethnicity, scene socioeconomic status, and documented clinical characteristics: altered mental status, hypotension, tachypnea, tachycardia, fever. We conducted a secondary analysis of patients who were positive for the quick sequential organ failure assessment (qSOFA) using first prehospital vital signs.

RESULTS

We analyzed 20,172 records for EMS-transported patients with diagnosed sepsis. Overall, 8% of patients were Black, 8% were Hispanic, and 72% were White. Prehospital sepsis recognition was 18%. Prehospital sepsis recognition was similar across racial and ethnic groups (Black: 17.2%, Hispanic: 17.4%, White: 18.1%) and adjusted odds of sepsis recognition did not differ between racial and ethnic groups. Overall mortality was 11% (2,186). Prehospital sepsis recognition was associated with a 18% reduction in adjusted odds of mortality (OR: 0.82, 95% CI: 0.70-0.94). Of patients who were qSOFA positive in the field (n = 2,168), EMS sepsis recognition was 32% and was similar across race and ethnicities. Adjusted odds of mortality were 0.68 (95% CI: 0.53-0.88) when sepsis was recognized in the prehospital setting.

CONCLUSION

EMS identified sepsis in fewer than one in three patients even after limiting to those positive for qSOFA, without differences by race and ethnicity. EMS sepsis recognition was associated with reduced odds of mortality; however, Black patients remained at greater odds of death suggesting additional factors that warrant investigation.

Prehospital early warning scores for adults with suspected sepsis: retrospective diagnostic cohort study

BACKGROUND

Ambulance services need to identify and prioritise patients with sepsis for early hospital assessment. We aimed to determine the accuracy of early warning scores alongside paramedic diagnostic impression to identify sepsis that required urgent treatment.

METHODS

We undertook a retrospective diagnostic cohort study involving adult emergency medical cases transported to Sheffield Teaching Hospitals ED by Yorkshire Ambulance Service in 2019. We used routine ambulance service data to calculate 21 early warning scores and categorise paramedic diagnostic impressions as sepsis, infection, non-specific presentation or other presentation. We linked cases to hospital records and identified those meeting the sepsis-3 definition who received urgent hospital treatment for sepsis (reference standard). Analysis determined the accuracy of strategies that combined early warning scores at varying thresholds for positivity with paramedic diagnostic impression.

RESULTS

We linked 12 870/24 955 (51.6%) cases and identified 348/12 870 (2.7%) with a positive reference standard. None of the strategies provided sensitivity greater than 0.80 with positive predictive value greater than 0.15. The area under the receiver operating characteristic curve for the National Early Warning Score, version 2 (NEWS2) applied to patients with a diagnostic impression of sepsis or infection was 0.756 (95% CI 0.729, 0.783). No other early warning score provided clearly superior accuracy to NEWS2. Paramedic impression of sepsis or infection had sensitivity of 0.572 (0.519, 0.623) and positive predictive value of 0.156 (0.137, 0.176). NEWS2 thresholds of >4, >6 and >8 applied to patients with a diagnostic impression of sepsis or infection, respectively, provided sensitivities and positive predictive values of 0.522 (0.469, 0.574) and 0.216 (0.189, 0.245), 0.447 (0.395, 0.499) and 0.274 (0.239, 0.313), and 0.314 (0.268, 0.365) and 0.333 (0.284, 0.386).

CONCLUSION

No strategy is ideal but using NEWS2 alongside paramedic diagnostic impression of infection or sepsis could identify one-third to half of sepsis cases without prioritising unmanageable numbers. No other score provided clearly superior accuracy to NEWS2.

TRIAL REGISTRATION NUMBER

researchregistry5268, https://www.researchregistry.com/browse-the-registry%23home/registrationdetails/5de7bbd97ca5b50015041c33/.

Prediction of the Risk of Sepsis by Using Analysis of Plasma Glucose and Serum Lactate in Ambulance Services: A Prospective Study

INTRODUCTION

The early recognition of patients with sepsis is difficult and the initial assessment outside of hospitals is challenging for ambulance clinicians (ACs). Indicators that ACs can use to recognize sepsis early are beneficial for patient outcomes. Research suggests that elevated point-of-care (POC) plasma glucose and serum lactate levels may help to predict sepsis in the ambulance service (AS) setting.

STUDY OBJECTIVE

The aim of this study was to test the hypothesis that the elevation of POC plasma glucose and serum lactate levels may help to predict Sepsis-3 in the AS.

METHODS

A prospective observational study was performed in the AS setting of Gothenburg in Sweden from the beginning of March 2018 through the end of September 2019. The criteria for sampling POC plasma glucose and serum lactate levels in the AS setting were high or intermediate risk according to the Rapid Emergency Triage and Treatment System (RETTS), as red, orange, yellow, and green if the respiratory rate was >22 breaths/minutes. Sepsis-3 were identified retrospectively. A primary and secondary analyses were carried out. The primary analysis included patients cared for in the AS and emergency department (ED) and were hospitalized. In the secondary analysis, patients who were only cared for in the AS and ED without being hospitalized were also included. To evaluate the predictive ability of these biomarkers, the area under the curve (AUC), sensitivity, specificity, and predictive values were used.

RESULTS

A total of 1,057 patients were included in the primary analysis and 1,841 patients were included in the secondary analysis. In total, 253 patients met the Sepsis-3 criteria (in both analyses). The AUC for POC plasma glucose and serum lactate levels showed low accuracy in predicting Sepsis-3 in both the primary and secondary analyses. Among all hospitalized patients, regardless of Sepsis-3, more than two-thirds had elevated plasma glucose and nearly one-half had elevated serum lactate when measured in the AS.

CONCLUSIONS

As individual biomarkers, an elevated POC plasma glucose and serum lactate were not associated with an increased likelihood of Sepsis-3 when measured in the AS in this study. However, the high rate of elevation of these biomarkers before arrival in hospital highlights that their role in clinical decision making at this early stage needs further evaluation, including other endpoints than Sepsis-3.

Association between variables measured in the ambulance and in-hospital mortality among adult patients with and without infection: a prospective cohort study

BACKGROUND

Patients presenting with infection to the ambulance are common, but risk factors for poor outcome are not known. The primary aim of the current study was to study the association between variables measured in the ambulance and mortality among adult patients with and without infection. The secondary aim was to study the association between these variables and mortality in a subgroup of patients who developed sepsis within 36 h.

METHODS

Prospective cohort study of 553 ambulance patients with, and 318 patients without infection, performed in Stockholm during 2017-2018. The association between 21 variables (8 keywords related to medical history, 6 vital signs, 4 blood tests, and age, gender, comorbidity) and in-hospital mortality was analysed using logistic regression.

RESULTS

Among patients with infection, inability of the patient to answer questions relating to certain symptoms such as pain and gastrointestinal symptoms was significantly associated with mortality in univariable analysis, in addition to oxygen saturation < 94%, heart rate > 110 /min, Glasgow Coma Scale (GCS) < 15, soluble urokinase Plasminogen Activator Receptor (suPAR) 4.0-7.9 ng/mL, suPAR ≥ 8.0 ng/mL and a Charlson comorbidity score ≥ 5. suPAR ≥ 8.0 ng/mL remained significant in multivariable analysis (OR 25.4; 95% CI, 3.2-199.8). Among patients without infection, suPAR ≥ 8.0 ng/mL and a Charlson comorbidity score ≥ 5 were significantly associated with mortality in univariable analysis, while suPAR ≥ 8.0 ng/mL remained significant in multivariable analysis (OR 56.1; 95% CI, 4.5-700.0). Among patients who developed sepsis, inability to answer questions relating to pain remained significant in multivariable analysis (OR 13.2; 95% CI, 2.2-78.9), in addition to suPAR ≥ 8.0 ng/mL (OR 16.1; 95% CI, 2.0-128.6).

CONCLUSIONS

suPAR ≥ 8.0 ng/mL was associated with mortality in patients presenting to the ambulance both with and without infection and in those who developed sepsis. Furthermore, the inability of the ambulance patient with an infection to answer questions relating to specific symptoms was associated with a surprisingly high mortality. These results suggest that suPAR and medical history are valuable tools with which to identify patients at risk of poor outcome in the ambulance and could potentially signal the need of enhanced attention.

TRIAL REGISTRATION

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

Assessment of a Comparative Bayesian-Enhanced Population-Based Decision Model for COVID-19 Critical Care Prediction in the Dominican Republic Social Security Affiliates

Introduction: The novel coronavirus disease 2019 (COVID-19) has been a major health concern worldwide. This study aims to develop a Bayesian model to predict critical outcomes in patients with COVID-19.

Methods: Sensitivity and specificity were obtained from previous meta-analysis studies. The complex vulnerability index (IVC-COV2 index for its abbreviation in Spanish) was used to set the pretest probability. Likelihood ratios were integrated into a Fagan nomogram for posttest probabilities, and IVC-COV2 + National Early Warning Score (NEWS) values and CURB-65 scores were generated. Absolute and relative diagnostic gains (RDGs) were calculated based on pretest and posttest differences.

Results: The IVC-COV2 index was derived from a population of 1,055,746 individuals and was based on mortality in high-risk (71.97%), intermediate-risk (26.11%), and low-risk (1.91%) groups. The integration of models in which IVC-COV2 intermediate + NEWS ≥ 5 and CURB-65 > 2 led to a "number needed to (NNT) diagnose" that was slightly improved in the CURB-65 model (2 vs. 3). A comparison of diagnostic gains revealed that neither the positive likelihood ratio (P = 0.62) nor the negative likelihood ratio (P = 0.95) differed significantly between the IVC-COV2 NEWS model and the CURB-65 model.

Conclusion: According to the proposed mathematical model, the combination of the IVC-COV2 intermediate score and NEWS or CURB-65 score yields superior results and a greater predictive value for the severity of illness. To the best of our knowledge, this is the first population-based/mathematical model developed for use in COVID-19 critical care decision-making.

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 .

Clinical validation demonstrates concordance of qSOFA and POC lactate Bayesian model: Results from the ACDC Phase-2 program

Sepsis is a common and lethal medical problem. The objective of this study was to validate a Bayesian Model that integrates qSOFA and prehospital Lactate, with a comparison analysis from a real clinical data of patients with sepsis.

METHODS

We conducted a two tired validation study with one arm focusing on Bayesian modeling and a second retrospective observational arm addressing real data validation. For Bayesian modeling, sensitivity and specificity of prehospital lactate were attained from pooled meta-analysis data. Later, for clinical validation, we used data from 2016 to 2017 of ED patients diagnosed with sepsis. Pretest probabilities from qSOFA score where combined with prehospital lactate and inserted into a Bayesian model to calculate posttest probabilities. Absolute and relative diagnostic gains were calculated. Statistical significance was assessed via t-test, chi square and odds ratio. P value was set to be 0.05.

RESULTS

For the Bayesian arm; meta-analysis data for prehospital lactate resulted in a positive likelihood ratio (LR+) of 1.69 and negative likelihood ratio (LR-) of 0.44. Integration of lactate and qSOFA demonstrated significant post-test improvements. On the Clinical Validation arm, 1470 patients were included with 176 patients meeting analysis criteria. When comparing qSOFA + Abnormal Lactate vs qSOFA and normal Lactate, the ICU vs Non-ICU cohorts were statistically different (p < 0.01) Odds Ratio: 2.35 (95% CI [1.22-4.6]).

CONCLUSION

Bayesian mathematical model demonstrated that a qSOFA-based clinical decision can be complemented by the use of point of-care lactate. These results were confirmed by our clinical validation arm.

Screening strategies to identify sepsis in the prehospital setting: a validation study

BACKGROUND

In the prehospital setting, differentiating patients who have sepsis from those who have infection but no organ dysfunction is important to initiate sepsis treatments appropriately. We aimed to identify which published screening strategies for paramedics to use in identifying patients with sepsis provide the most certainty for prehospital diagnosis.

METHODS

We identified published strategies for screening by paramedics through a literature search. We then conducted a validation study in Alberta, Canada, from April 2015 to March 2016. For adult patients (≥ 18 yr) who were transferred by ambulance, we linked records to an administrative database and then restricted the search to patients with infection diagnosed in the emergency department. For each patient, the classification from each strategy was determined and compared with the diagnosis recorded in the emergency department. For all strategies that generated numeric scores, we constructed diagnostic prediction models to estimate the probability of sepsis being diagnosed in the emergency department.

RESULTS

We identified 21 unique prehospital screening strategies, 14 of which had numeric scores. We linked a total of 131 745 eligible patients to hospital databases. No single strategy had both high sensitivity (overall range 0.02-0.85) and high specificity (overall range 0.38-0.99) for classifying sepsis. However, the Critical Illness Prediction (CIP) score, the National Early Warning Score (NEWS) and the Quick Sepsis-Related Organ Failure Assessment (qSOFA) score predicted a low to high probability of a sepsis diagnosis at different scores. The qSOFA identified patients with a 7% (lowest score) to 87% (highest score) probability of sepsis diagnosis.

INTERPRETATION

The CIP, NEWS and qSOFA scores are tools with good predictive ability for sepsis diagnosis in the prehospital setting. The qSOFA score is simple to calculate and may be useful to paramedics in screening patients with possible sepsis.

A Bayesian decision support sequential model for severity of illness predictors and intensive care admissions in pneumonia

Background

Community-acquired pneumonia (CAP) is one of the leading causes of morbidity and mortality in the USA. Our objective was to assess the predictive value on critical illness and disposition of a sequential Bayesian Model that integrates Lactate and procalcitonin (PCT) for pneumonia.

Methods

Sensitivity and specificity of lactate and PCT attained from pooled meta-analysis data. Likelihood ratios calculated and inserted in Bayesian/ Fagan nomogram to calculate posttest probabilities. Bayesian Diagnostic Gains (BDG) were analyzed comparing pre and post-test probability. To assess the value of integrating both PCT and Lactate in Severity of Illness Prediction we built a model that combined CURB65 with PCT as the Pre-Test markers and later integrated the Lactate Likelihood Ratio Values to generate a combined CURB 65 + Procalcitonin + Lactate Sequential value.

Results

The BDG model integrated a CUBR65 Scores combined with Procalcitonin (LR+ and LR-) for Pre-Test Probability Intermediate and High with Lactate Positive Likelihood Ratios. This generated for the PCT LR+ Post-test Probability (POSITIVE TEST) Posterior probability: 93% (95% CI [91,96%]) and Post Test Probability (NEGATIVE TEST) of: 17% (95% CI [15–20%]) for the Intermediate subgroup and 97% for the high risk sub-group POSITIVE TEST: Post-Test probability:97% (95% CI [95,98%]) NEGATIVE TEST: Post-test probability: 33% (95% CI [31,36%]) . ANOVA analysis for CURB 65 (alone) vs CURB 65 and PCT (LR+) vs CURB 65 and PCT (LR+) and Lactate showed a statistically significant difference (P value = 0.013).

Conclusions

The sequential combination of CURB 65 plus PCT with Lactate yielded statistically significant results, demonstrating a greater predictive value for severity of illness thus ICU level care.

HEART Score and Stress Test Emergency Department Bayesian Decision Scheme: Results from the Acute Care Diagnostic Collaboration

BACKGROUND

Accurate identification of patients at risk of major adverse cardiac events (MACE) places a substantial burden on emergency physicians (EPs). Bayesian nomogram for risk stratification in low- to intermediate-risk cardiovascular patients has not been investigated previously.

OBJECTIVE

The objective of this study was to develop a comparative diagnostic model using Bayesian statistics for exercise treadmill test (ETT) and stress echocardiogram (ECHO) to calculate post-test diagnostic risk of MACE using HEART (history, electrocardiogram, age, risk factors, and troponin) risk score as predictor of pretest probability.

METHODS

Stratification was made by applying HEART scores for the prediction of MACE. Likelihood ratios (LR) were calculated using pooled sensitivity and specificity of ETT and ECHO from the American College of Cardiology Foundation/American Heart Association systematic review. Post-test probabilities were obtained after inserting HEART score and LR into Bayesian nomogram. Analysis of variance was used to assess statistical association.

RESULTS

Positive LR (LR+) for ETT was 4.56 and negative LR (LR-) was 0.27; for ECHO, LR+ 5.65 and LR- 0.15. Bayesian statistical modeling post-test probabilities for LR+ and low HEART risk yielded a post-test probability for ETT of 7.75% and 9.09% for ECHO; intermediate risk gave 47.62% and 52.63%, respectively. For LR-, low HEART risk post-test probability for ETT was 0.46% and for ECHO 0.26%; intermediate risk probabilities were 4.48% and 2.49%, respectively. LR- was statistically significant in ruling out MACE with ECHO (p < 0.001), but no significant differences were seen for LR+ (p = 0.64).

CONCLUSIONS

This Bayesian analysis demonstrated slight superiority of stress ECHO over ETT in low- and intermediate-risk patients in ruling out MACE.

Prehospital Sepsis Care

Prehospital care providers are tasked with the delivery of time-sensitive care, and emergency medical services (EMS) systems must match patients to appropriate clinical resources. Modern systems are uniquely positioned to recognize and treat patients with sepsis. Interventions such as administration of intravenous fluid and transporting patients to the appropriate level of definitive care are linked to improved patient outcomes. As EMS systems refine their protocols for the recognition and stabilization of patients with suspected or presumed sepsis, EMS providers need to be educated about the spectrum of sepsis-related presentations and treatment strategies need to be standardized.

Bayesian comparative assessment of diagnostic accuracy of low-dose CT scan and ultrasonography in the diagnosis of urolithiasis after the application of the STONE score

OBJECTIVE

The objective of our study was to assess the diagnostic quality of low-dose computed tomography (CT) when compared to ultrasound (US) in diagnosis of urolithiasis using STONE score as a predictor of pre-test probability and the Bayesian statistical model to calculate post-test probabilities (POST) for both diagnostic tests.

METHODS

STONE score was used to form risk groups to obtain pre-test probabilities. Likelihood ratios (LR) were calculated from external data for low-dose CT and US. POST were obtained using pre-test probabilities and likelihood ratios with Bayesian nomogram. Absolute (ADG) and relative (RDG) gains in diagnostic value were calculated.

RESULTS

Calculated +LR for US was 12 and -LR was 0.32; for CT, +LR was 19 and -LR 0.04. +LR and low STONE for US yielded POST 57% and RDG 470%; intermediate STONE POST 92% and RDG 84%; and high STONE POST 99% and RDG 10%. -LR and low STONE for US POST 3% and RDG -70%; intermediate POST 24% and RDG -52%; and high STONE POST 74% and RDG -17.7%. +LR and low STONE for CT POST 68% and RDG 580%; moderate STONE POST 95% and RDG 90%; and high STONE POST 99% and RDG 10%. -LR and low STONE for CT POST 0% and RDG -100%; intermediate POST 4% and RDG -92%; and high STONE POST 26% and RDG -71.1%. ANOVA calculations comparing CT vs US for +LR showed no statistical significance (P value = 0.9893; LR- P value = 0.5488).

CONCLUSION

Bayesian statistical analysis demonstrated slight superiority of CT scan over US on STONE score low- and moderate-risk stratified subtypes, whereas no significant advantage was seen when evaluating high-probability patients.

Bayesian comparative model of CT scan and ultrasonography in the assessment of acute appendicitis: results from the Acute Care Diagnostic Collaboration project

The objective of this study was to develop a comparative diagnostic model for computed tomography (CT) and ultrasound (US) in the assessment of acute appendicitis using Alvarado risk score as a predictor of pretest probability and Bayesian statistical model as a tool to calculate posttest probabilities for both diagnostic test. Stratification was made by applying the Alvarado score for the prediction of acute appendicitis. Likelihood ratios were calculated using sensitivity and specificity of both CT and US from a Meta-analysis. Posttest probabilities were obtained after inserting Alvarado score and likelihood ratios into Bayesian nomogram. Absolute and relative gains were calculated. ANOVA was used to assess statistical association. 4341 patients from 31 studies yielded a pooled sensitivity and specificity US of 83% (95% CI, 78%-87%) and 93% (95% CI, 90%-96%) and 94% (95% CI, 92%-95%) and 94% (95% CI, 94%-96%), respectively, for CT studies. Positive likelihood ratios (LR) for US were 12 and negative LR was 0.18; for CT +LR was 16 and -LR 0.06. Bayesian statistical modeling posttest probabilities for +LR and low Alvarado risk results yielded a posttest probability for US of 83.72% and 87.27% for CT, intermediate risk gave 95.88% and 96.88%, high risk 99.37% and 99.53 respectively. No statistical differences were found between Ultrasound and CT. This Bayesian analysis demonstrated slight superiority of CT scan over US low-risk patients, whereas no significant advantage was seen when evaluating intermediate and high risk patients. This study also favored elevated accuracy of the Alvarado score.