scholarly journals Predictors and their prognostic value for no ROSC and mortality after a non-cardiac surgery intraoperative cardiac arrest: a retrospective cohort study

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Matheus F. Vane ◽  
Maria J. C. Carmona ◽  
Sergio M. Pereira ◽  
Karl B. Kern ◽  
Sérgio Timerman ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Prognostic Value ◽  
Laboratory Data ◽  
Spontaneous Circulation ◽  
Likelihood Ratios ◽  
Receiver Operating Characteristic Curves ◽  
Post Test ◽  
Verbal Component ◽  
Sensitivity Specificity ◽  
Test Probability

Abstract Data on predictors of intraoperative cardiac arrest (ICA) outcomes are scarce in the literature. This study analysed predictors of poor outcome and their prognostic value after an ICA. Clinical and laboratory data before and 24 hours (h) after ICA were analysed as predictors for no return of spontaneous circulation (ROSC) and 24 h and 1-year mortality. Receiver operating characteristic curves for each predictor and sensitivity, specificity, positive and negative likelihood ratios, and post-test probability were calculated. A total of 167,574 anaesthetic procedures were performed, including 158 cases of ICAs. Based on the predictors for no ROSC, a threshold of 13 minutes of ICA yielded the highest area under curve (AUC) (0.867[0.80–0.93]), with a sensitivity and specificity of 78.4% [69.6–86.3%] and 89.3% [80.4–96.4%], respectively. For the 1-year mortality, the GCS without the verbal component 24 h after an ICA had the highest AUC (0.616 [0.792–0.956]), with a sensitivity of 79.3% [65.5–93.1%] and specificity of 86.1 [74.4–95.4]. ICA duration and GCS 24 h after the event had the best prognostic value for no ROSC and 1-year mortality. For 24 h mortality, no predictors had prognostic value.

scholarly journals Value and Diagnostic Efficacy of Fetal Morphology Assessment Using Ultrasound in A Poor-Resource Setting

Diagnostics ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 109 ◽  
Author(s):  
Ukweh ◽  
Ugbem ◽  
Okeke ◽  
Ekpo
Keyword(s):  
Predictive Value ◽  
Performance Metrics ◽  
Area Under The Curve ◽  
Youden Index ◽  
Likelihood Ratios ◽  
Diagnostic Efficacy ◽  
Predictive Values ◽  
Resource Setting ◽  
Post Test ◽  
Sensitivity Specificity

Background: Ultrasound is operator-dependent, and its value and efficacy in fetal morphology assessment in a low-resource setting is poorly understood. We assessed the value and efficacy of fetal morphology ultrasound assessment in a Nigerian setting. Materials and Methods: We surveyed fetal morphology ultrasound performed across five facilities and followed-up each fetus to ascertain the outcome. Fetuses were surveyed in the second trimester (18th–22nd weeks) using the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG) guideline. Clinical and surgical reports were used as references to assess the diagnostic efficacy of ultrasound in livebirths, and autopsy reports to confirm anomalies in terminated pregnancies, spontaneous abortions, intrauterine fetal deaths, and still births. We calculated sensitivity, specificity, positive and negative predictive values, Area under the curve (AUC), Youden index, likelihood ratios, and post-test probabilities. Results: In total, 6520 fetuses of women aged 15–46 years (mean = 31.7 years) were surveyed. The overall sensitivity, specificity, and AUC were 77.1 (95% CI: 68–84.6), 99.5 (95% CI: 99.3–99.7), and 88.3 (95% CI: 83.7–92.2), respectively. Other performance metrics were: positive predictive value, 72.4 (95% CI: 64.7–79.0), negative predictive value, 99.6 (95% CI: 99.5–99.7), and Youden index (77.1%). Abnormality prevalence was 1.67% (95% CI: 1.37–2.01), and the positive and negative likelihood ratios were 254 (95% CI: 107.7–221.4) and 0.23 (95% CI: 0.16–0.33), respectively. The post-test probability for positive test was 72% (95% CI: 65–79). Conclusion: Fetal morphology assessment is valuable in a poor economics setting, however, the variation in the diagnostic efficacy across facilities and the limitations associated with the detection of circulatory system anomalies need to be addressed.

scholarly journals A “Clinician’s Probability Calculator” to convert pre-test to post-test probability of COVID-19 , based on method validation from each laboratory

2020 ◽  
Author(s):  
Zoe Brooks ◽  
Saswati Das ◽  
Tom Pliura
Keyword(s):  
Health Staff ◽  
Test Results ◽  
Likelihood Ratios ◽  
Percent Agreement ◽  
Public Health Staff ◽  
Post Test ◽  
Online Calculator ◽  
Test Result ◽  
Post Test Probability ◽  
Test Probability

During coronavirus pandemic testing and identifying the virus has been a unique and constant challenge for the scientific community. In this paper, we discuss a practical solution to help guide clinicians and public health staff with the interpretation of the probability that a positive, or negative, COVID-19 test result indicates an infected person, based on their clinical estimate of pre-test probability of infection. The LinkedIn survey confirmed that the pre-test probability of COVID-19 increases with patient age, known contact, and severity of symptoms, as well as prevalence of disease in the local population. PPA (Positive Percent Agreement, PPA) and NPA (Negative Percent Agreement, specificity), differ between individual methods. Results vary between laboratories and the manufacturer for the same method. The confidence intervals of results vary with the number of samples tested, often adding a large range of possibilities to the reported test result. The online calculator met the objective.The authors postulated that the clinical pre-test probability of COVID-19 increases relative to local prevalence of disease plus patient age, known contact, and severity of symptoms. We conducted a small survey on LinkedIn to confirm that hypothesis. We examined results of PPA (Positive Percent Agreement, sensitivity) and NPA (Negative Percent Agreement, specificity) from 73 individual laboratory experiments for molecular tests for SARS-CoV-2as reported to the FIND database,(1) and for selected methods in FDA EUA submissions (2,3). We calculated likelihood ratios to convert pre-test to post-test probability of disease, then further calculated the number of true and false results expected in every ten positive or negative test results, plus an estimate that one in ‘x’ test results is true. We designed an online calculator to create graphics and text to fulfill the objective. A positive or negative test result from one laboratory conveys a higher probability for the presence or absence of COVID-19 than the same result from another laboratory, depending on clinical pre-test probability of disease plus proven method PPA and NPA in each laboratory. Likelihood ratios and confidence intervals provide valuable information but are seldom used in clinical settings. We recommend that testing laboratories verify PPA and NPA, and utilize a tool such as the “Clinician’s Probability Calculator” to verify acceptable test performance and create reports to help guide clinicians and public health staff with estimation of post-test probability of COVID-19 .

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

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Amado Alejandro Baez ◽  
Laila Cochon ◽  
Jose Maria Nicolas
Keyword(s):  
Predictive Value ◽  
Severity Of Illness ◽  
Positive Test ◽  
Bayesian Decision ◽  
Likelihood Ratios ◽  
Negative Test ◽  
Significant Difference ◽  
Post Test ◽  
Post Test Probability ◽  
Test Probability

Abstract 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.

The diagnostic accuracy of wrist cineradiography in diagnosing scapholunate dissociation

2013 ◽  
Vol 39 (3) ◽  
pp. 263-271 ◽  
Author(s):  
G. S. I. Sulkers ◽  
N. W. L. Schep ◽  
M. Maas ◽  
C. M. A. M. van der Horst ◽  
J. C. Goslings ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Predictive Value ◽  
Diagnostic Value ◽  
Inclusion Criteria ◽  
Scapholunate Ligament ◽  
Scapholunate Dissociation ◽  
Post Test ◽  
Sensitivity Specificity ◽  
Post Test Probability ◽  
Test Probability

Ruptures of the scapholunate ligament (SLL) may cause carpal instability, also known as scapholunate dissociation (SLD). SLD may lead to osteoarthritis of the radiocarpal and midcarpal joints. The aim of this retrospective study was to determine the diagnostic value of wrist cineradiography in detecting SLD. All cineradiographic studies made during a 24 year period were retrieved. All patients who underwent the confirmation method (arthroscopy and/or arthrotomy) and cineradiography were included. In total, 84 patients met the inclusion criteria. Sensitivity, specificity, likelihood ratio, positive predictive value, negative predictive value, and diagnostic accuracy for detecting SLD were calculated for radiography and cineradiography. Cineradiography had a sensitivity of 90%, a specificity of 97%, and a diagnostic accuracy of 0.93 in detecting SLD. Radiography had a sensitivity of 81%, a specificity of 80%, and a diagnostic accuracy of 0.81. Cineradiography has a high diagnostic value for diagnosing SLDs. A positive cineradiography markedly increases the post-test probability of SLD.

Diagnostic accuracy of three clinical dehydration scales: a systematic review

2017 ◽  
Vol 103 (4) ◽  
pp. 383-388 ◽  
Author(s):  
Anna Falszewska ◽  
Hania Szajewska ◽  
Piotr Dziechciarz
Keyword(s):  
Diagnostic Accuracy ◽  
Low Income ◽  
Positive Likelihood Ratio ◽  
High Income ◽  
Low Income Countries ◽  
Severe Dehydration ◽  
Post Test ◽  
Sensitivity Specificity ◽  
Post Test Probability ◽  
Test Probability

ObjectiveTo systematically assess the diagnostic accuracy of the Clinical Dehydration Scale (CDS), the WHO Scale and the Gorelick Scale in identifying dehydration in children with acute gastroenteritis (AGE).DesignThree databases, two registers of clinical trials and the reference lists from identified articles were searched for diagnostic accuracy studies in children with AGE. The index tests were the CDS, WHO Scale and Gorelick Scale, and reference standard was the percentage loss of body weight. The main analysed outcomes were the sensitivity, specificity, positive likelihood ratio (LR) and negative LR.ResultsTen studies were included. In high-income countries, the CDS provided a moderate-to-large increase in the post-test probability of predicting moderate to severe (≥6%) dehydration (positive LR 3.9–11.79), but it was of limited value for ruling it out (negative LR 0.55–0.71). In low-income countries, the CDS showed limited value both for ruling in and ruling out moderate-to-severe dehydration. In both settings, the CDS showed poor diagnostic accuracy for ruling in or out no dehydration (<3%) or some dehydration (3%–6%). The WHO Scale showed no or limited value in assessing dehydration in children with diarrhoea. With one exception, the included studies did not confirm the diagnostic accuracy of the Gorelick Scale.ConclusionLimited evidence suggests that the CDS can help in ruling in moderate-to-severe dehydration (≥6%) in high-income settings only. The WHO and Gorelick Scales are not helpful for assessing dehydration in children with AGE.

scholarly journals Prognostic value of changes in the cardiac arrest rhythms from the prehospital stage to the emergency department in out-of-hospital cardiac arrest patients without prehospital returns of spontaneous circulation: A nationwide observational study

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257883
Author(s):  
Jae Guk Kim ◽  
Hyungoo Shin ◽  
Jun Hwi Cho ◽  
Hyun Young Choi ◽  
Wonhee Kim ◽  
...  
Keyword(s):  
Emergency Department ◽  
Cardiac Arrest ◽  
Propensity Score ◽  
Hospital Discharge ◽  
Prognostic Value ◽  
Spontaneous Circulation ◽  
Shock Group ◽  
Electrical Shock ◽  
Neurological Outcomes ◽  
Hospital Cardiac Arrest

Background This study aimed to assess the prognostic value of the changes in cardiac arrest rhythms from the prehospital stage to the ED (emergency department) in out-of-hospital cardiac arrest (OHCA) patients without prehospital returns of spontaneous circulation (ROSC). Methods This retrospective analysis was performed using nationwide population-based OHCA data from South Korea between 2012 and 2016. Patients with OHCA with medical causes and without prehospital ROSC were included and divided into four groups according to the nature of their cardiac arrest rhythms (shockable or non-shockable) in the prehospital stage and in the ED: (1) the shockable and shockable (Shock-Shock) group, (2) the shockable and non-shockable (Shock-NShock) group, (3) the non-shockable and shockable (NShock-Shock) group, and (4) the non-shockable and non-shockable (NShock-NShock) group. The presence of a shockable rhythm was confirmed based on the delivery of an electrical shock. Propensity score matching and multivariate logistic regression analyses were used to assess the effect of changes in the cardiac rhythms on patient outcomes. The primary outcome was sustained ROSC in the ED; the secondary outcomes were survival to hospital discharge and good neurological outcomes at hospital discharge. Results After applying the exclusion criteria, 51,060 eligible patients were included in the study (Shock-Shock, 4223; Shock-NShock, 3060; NShock-Shock, 11,509; NShock-NShock, 32,268). The propensity score-matched data were extracted from the six comparative subgroups. For sustained ROSC in the ED, Shock-Shock showed a higher likelihood than Shock-NShock (P <0.01) and NShock-NShock (P <0.01), Shock-NShock showed a lower likelihood than NShock-Shock (P <0.01) and NShock-NShock (P <0.01), NShock-Shock showed a higher likelihood NShock-NShock (P <0.01). For survival to hospital discharge, Shock-Shock showed a higher likelihood than Shock-NShock (P <0.01), NShock-Shock (P <0.01), and NShock-NShock (P <0.01), Shock-NShock showed a higher likelihood than NShock-Shock (P <0.01) and NShock-NShock (P <0.01), of sustained ROSC in the ED. For good neurological outcomes, Shock-Shock showed higher likelihood than Shock-NShock (P <0.01), NShock-Shock (P <0.01), and NShock-NShock (P <0.01), Shock-NShock showed better likelihood than NShock-NShock (P <0.01), NShock-Shock showed a better likelihood than NShock-NShock (P <0.01). Conclusion Sustained ROSC in the ED may be expected for patients with shockable rhythms in the ED compared with those with non-shockable rhythms in the ED. For the clinical outcomes, survival to hospital discharge and neurological outcomes, patients with Shock-Shock showed the best outcome, whereas patients with NShock-NShock showed the poorest outcome and Shock-NShock showed a higher likelihood of achieving survival to hospital discharge with no significant differences in the neurological outcomes compared with NShock-Shock.

scholarly journals Hip aspiration culture: analysing data from a single operator series investigating periprosthetic joint infection

2021 ◽  
Vol 6 (6) ◽  
pp. 165-170
Author(s):  
Connor J. Barker ◽  
Alan Marriot ◽  
Munir Khan ◽  
Tamsin Oswald ◽  
Samuel J. Tingle ◽  
...  
Keyword(s):  
Periprosthetic Joint Infection ◽  
Joint Infection ◽  
Surgical Care ◽  
Comparison Results ◽  
Significant Difference ◽  
Post Test ◽  
Single Operator ◽  
Sensitivity Specificity ◽  
Post Test Probability ◽  
Test Probability

Abstract. Introduction: We undertook this study to know the sensitivity, specificity and post-test probabilities of hip aspiration when diagnosing periprosthetic hip infections. We also examined “dry tap” (injection with saline and aspiration) results and aspiration volumes. Methods: This is a retrospective cohort study of patients aspirated for suspected periprosthetic joint infection between July 2012 and October 2016. All aspirations were carried out by one trained surgical care practitioner (SCP). All aspirations followed an aseptic technique and fluoroscopic guidance. Aspiration was compared to tissue biopsy taken at revision. Aspiration volumes were analysed for comparison. Results: Between January 2012 and September 2016, 461 hip aspirations were performed by our SCP. Of these 125 progressed to revision. We calculated sensitivity 59 % (confidence interval (CI) 35 %–82 %) and specificity 94 % (CI 89 %–98 %). Pre-test probability for our cohort was 0.14. Positive post-test probability was 0.59 and negative post-test probability 0.06. Aspiration volume for infected (n=17) and non-infected (n=108) joints was compared and showed no significant difference. Dry taps were experienced five times; in each instance the dry tap agreed with the biopsy result. Conclusions: Our data show that hip aspiration culture is a highly specific investigation for diagnosing infection but that it is not sensitive. Aspiration volume showed no significant difference between infected and non-infected groups. Each time a joint was infiltrated with saline to achieve a result, the result matched tissue sampling.

scholarly journals “Clinician’s Probability Calculator” to Convert Pre-Test to Post-Test Probability of COVID-19, Based on Method Validation from Each Laboratory

2021 ◽  
Author(s):  
Zoe Brooks ◽  
Saswati Das ◽  
Tom Pliura
Keyword(s):  
Test Performance ◽  
False Negative ◽  
Likelihood Ratios ◽  
Unique Challenge ◽  
Molecular Tests ◽  
Percent Agreement ◽  
Post Test ◽  
Test Result ◽  
Post Test Probability ◽  
Test Probability

Identifying the SARS-CoV-2 virus has been a unique challenge for the scientific community. In this paper, we discuss a practical solution to help guide clinicians with interpretation of the probability that a positive, or negative, COVID-19 test result indicates an infected person, based on their clinical estimate of pre-test probability of infection.The authors conducted a small survey on LinkedIn to confirm that hypothesis that that the clinical pre-test probability of COVID-19 increases relative to local prevalence of disease plus patient age, known contact, and severity of symptoms. We examined results of PPA (Positive Percent Agreement, sensitivity) and NPA (Negative Percent Agreement, specificity) from 73 individual laboratory experiments for molecular tests for SARS-CoV-2 as reported to the FIND database 1, and for selected methods in FDA EUA submissions2,3. Authors calculated likelihood ratios to convert pre-test to post-test probability of disease and designed an online calculator to create graphics and text to report results. Despite best efforts, false positive and false negative Covid-19 test results are unavoidable4,5. A positive or negative test result from one laboratory has a different probability for the presence of disease than the same result from another laboratory. Likelihood ratios and confidence intervals can convert the physician or other healthcare professional&rsquo;s clinical estimate of pre-test probability to post-test probability of disease. Ranges of probabilities differ depending on proven method PPA and NPA in each laboratory. We recommend that laboratories verify PPA and NPA and utilize a the &ldquo;Clinician&rsquo;s Probability Calculator&rdquo; to verify acceptable test performance and create reports to help guide clinicians with estimation of post-test probability of COVID-19.

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