Predictive criteria for acute heart failure in emergency department patients with acute dyspnoea: the PREDICA study
Jérôme RoncalliFabienne PicardNicholas DelarcheI. FaureCatherine PradeauM. ThicoïpéMichel GalinierSandrine Charpentier
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Objectives The early identification of patients with Acute Heart Failure Syndrome (AHFS) among patients admitted to the Emergency Department (ED) with dyspnoea can facilitate the introduction of appropriate treatments. The objectives are to identify the predictive factors for AHFS diagnosis in patients with acute dyspnoea (primary objective) and the clinical ‘gestalt’ (secondary objective) in ED. Methods PREDICA is an observational, prospective, multicentre study. The enrolment of patients admitted to the ED for nontraumatic acute dyspnoea and data collection on admission were recorded by the patient’s emergency physician. The AHFS endpoints were assessed following a duplicate expert evaluation by pairs of cardiologists and emergency physicians. Step-by-step logistic regression was used to retain predictive criteria, and the area under the receiver operating characteristic (ROC) curve of the model was constructed to assess the ability of the selected factors to identify real cases. The probability of AHFS was estimated on a scale from 1 to 10 based on the emergency physician’s perception and understanding (gestalt). Results Among 341 patients consecutively enrolled in three centres, 149 (44%) presented AHFS. Eight predictive factors of AHFS were detected with a performance test showing an area under the model ROC curve of 0.86. Gestalt greater than or equal to five showed sensitivity of 78% and specificity of 90% (AUC 0.91) and diagnosed 88% of AHF in our population. Conclusions We identified several independant predictors of final AHFS diagnosis. They should contribute to the development of diagnostic strategies in ED. However, unstructured gestalts seem to perform very well alone.Background An accurate pre‐operative risk assessment could reduce morbidity and mortality for high‐risk surgical patients. The aim of the study was to implement and preliminary validate a new score that could predict the occurrence of post‐operative complications ( PoCs ): the A nesthesiological and S urgical P ostoperative R isk A ssessment ( ASPRA ) score. Methods The ASPRA score was created through a literature's review; a score of 1–3 was given to each identified risk factor, according to its statistical correlation with PoC . ASPRA was retrospectively applied to a derivation set of 176 surgical patients. A receiver operating characteristic ( ROC ) analysis evaluated the discriminating ability of the score and cutoff value in predicting the occurrence of PoCs , according to the C lavien‐ D indo classification of surgical complications. The statistical validation of the score and related cutoff values was prospectively ran within a validation set of 1928 surgical patients. Results Through ROC analysis, an ASPRA score of 7 was chosen as the cutoff value in the derivation set. In the validation set, 65.3% of patients presented a PoC (Clavien ≥ 1). In this group, ROC analysis showed an area under the curve ( AUC ) of 0.72, and although potentially related to the high rate of complications a high positive predictive value of 87.0% has been observed. No significant differences were found in ROC‐AUC , sensitivity, specificity, or positive or negative predictive value between the derivation and validation sets ( P > 0.05). Conclusion The new ASPRA score has a high positive predictive value to predict the occurrence of PoCs . Further prospective studies are required to confirm these results.
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The aim of this study is to identify the inflammatory markers which predict a tubo-ovarian abscess (TOA) in the most accurate way.This study involves 312 women. Preoperative inflammatory markers in the study group were compared with those in the healthy control group to identify the most efficient predictor of TOA with a high sensitivity and specificity. The recommended cutoff values of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), white blood cell count, and red cell distribution width were determined using receiver operating characteristic curve (ROC) analyses.The area under the curve (AUC = 0.99) in the ROC analysis was found to be statistically significant for NLR (p < 0.001) with a cutoff value of ≥4.15 (95% CI 0.97-1.00, sensitivity 95.2%, specificity 99.4%). The positive predictive value of NLR was 99.2%, and the negative predictive value was 96.7% (p < 0.001). The recommended threshold for PLR was found to be 164.37 (AUC = 0.95, 95% CI 0.93-0.98, sensitivity 86.7%, specificity 92%), and the cutoff point of the white blood cell count in the ROC analysis was 9.55 × 103/μl (AUC = 0.90, 95% CI 0.87-0.95, sensitivity 78.68%, specificity 96.68%).Preoperative NLR and PLR improve the predictive value of serum markers for the presence of TOA.
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The purposes of this study were to develop and implement an observational training program and to assess the effects of a video observational training program on video and live observational proficiency. Physical education majors took a pretest in both a video and a live environment to assess observational proficiency. The task was observing children batting and answering questions regarding the critical features of the movement. The students were then placed into either a treatment ( n = 12) or a control ( n = 11) group. There were no differences between groups on either assessment ( p > .05). The treatment group then participated in a video observational training program. After the training, all subjects took a posttest in each environment to assess observational proficiency. The training was found to be effective in improving video observational proficiency ( p < .05) but not live observational proficiency ( p > .05). These results provide support for the effectiveness of video observational training in developing video observational proficiency but not live observational proficiency.
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Abstract Objectives This article prospectively examines the use of ultrasound for antenatal detection of abnormal placental cord insertion (PCI) and compares the antenatal classification with delivered placental classification. Study Design This prospective cohort study examined 277 singleton pregnancies in a tertiary center. Scans were performed between 10 and 14, 18 and 22, and 32 and 34 weeks where PCI site was identified and its shortest distance to margin measured. Standardized images of delivered placentas were taken and digitally measured. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of antenatal classification compared with delivered placental classification were calculated. Results Abnormal PCI (distance < 2 cm from margin) was confirmed in 30/277 (11%) placentas at delivery. Note that 102/277 (37%) of PCI sites were classified as abnormal in the first trimester (T1), 43/277 (16%) in the second trimester (T2), and 28/277 (10%) in the third trimester (T3). Sensitivity (73%) and specificity (91%) were highest at T2. The PPVs were low (22% in T1, 51% in T2, and 64% in T3) and the NPVs were high (96% in T1 and 97% in both T2 and T3) for all scans. Conclusion Abnormal PCI can be detected antenatally with optimal agreement with postnatal classification in T2. However, the incidence is overestimated at early scans with low PPVs.
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To analyse the diagnostic accuracy of inflammatory variables in patients operated on for suspected acute appendicitis. Open prospective population based study. Teaching hospital, Norway. Appendicectomy in 544 patients with clinically suspected acute appendicitis. Diagnostic accuracy of inflammatory variables using receiver operating characteristic (ROC) curve analysis. Logistic regression model of inflammatory variables using results of ROC-analysis. A small area under the ROC curve (AUC) (between 0.56 and 0.69) indicated less diagnostic accuracy. The best cut-off values were associated with low sensitivity and specificity, varying from 46% to 88%. Age, duration of history, and histological grade of inflammation significantly influenced the test results (AUC > 0.5). The white cell count (WCC) and C-reactive protein (CRP) concentration were independent predictors of acute appendicitis with cut-off values of > 12.3 × 109/L and >0 mg/L, respectively, but AUC values of over 0.5 were observed only in patients between 13 and 40 years of age. Inflammatory variables added information of limited value in the diagnosis of suspected acute appendicitis. The test results should be interpreted differently in different groups of patients.
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Studies can be observational or experimental. With an observational study, the investigator does not determine the assignment of subjects, and there might not be a control group. If there is a control group, assignment of the independent variable (exposure or intervention) is not under the control of the investigator. Observational studies can be rigorously conducted, but the lack of random assignment of the exposure/intervention introduces confounding and bias. Thus, the quality of evidence resulting from observational studies is lower than that of experimental randomized controlled trials (RCTs). An observational study might be performed if an RCT is unethical, impractical, or outside the control of the investigator. There are many types of prospective and retrospective observational study designs. However, an observational study design should be avoided if an experimental study is possible. Sophisticated statistical approaches can be used, but this does not elevate an observational study to the level of an RCT. Regardless of quality, an observational study cannot establish causality.
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The ABCD(2) score predicts the early risk of stroke after transient ischemic attack (TIA). However, data on the severity of recurrent events would also be useful. Do patients with high scores also have more severe early recurrent strokes, perhaps further justifying hospital admission? Do patients with low scores have a low early risk of recurrent TIA as well as recurrent stroke?We completed a prospective, population-based study in Oxfordshire, England, of 500 consecutive patients presenting with TIA from April 1, 2002, by using multiple methods of case ascertainment (Oxford Vascular Study). Recurrent TIA, minor stroke, and major stroke (National Institutes of Health Stroke Scale score >3 at the time of first assessment) were identified by face-to-face follow-up. Predictive value was expressed as the area under the receiver operating characteristic curve.Of 500 patients with TIA, 55 had a recurrent TIA (11.0%; 95% CI, 8.3% to 13.7%) and 50 had a recurrent stroke (10.0%; 95% CI, 7.5% to 12.0%) within 7 days. The ABCD(2) score was highly predictive of major recurrent stroke (area under the receiver operating characteristic curve=0.80; 95% CI, 0.72 to 0.87, P<0.0001), weakly predictive of minor stroke (area under the receiver operating characteristic curve=0.57; 95% CI, 0.43 to 0.71, P=0.26), and inversely related to risk of recurrent TIA (area under the receiver operating characteristic curve=0.37; 95% CI, 0.29 to 0.44, P=0.001) (overall heterogeneity, P<0.0001). The score predicted stroke-related disability, length of stay for recurrent stroke, and hence, overall acute hospital care costs.The ABCD(2) score predicts severity of recurrent events after TIA, high scores being associated with major recurrent stroke and low scores with high rates of recurrent TIA. These findings have implications for cost-benefit analyses of policies on hospital admission for patients with high scores and for the advice given to patients with low scores.
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In Brief BACKGROUND Discriminatory capabilities of a measurement technique can be assessed by a receiver operating characteristic (ROC) curve analysis (specifically, area under the curve [AUC]) and predictive modeling (predictive accuracy and positive predictive value). Theoretically, predictive accuracy is dependent on disease prevalence while AUC assessments are not. OBJECTIVE To compare the effect of changes in disease prevalence on ROC AUC analysis and predictive modeling. METHODS For this comparison, a data set with 72 individuals with coronary artery disease (CAD) and 1,857 individuals without CAD was used. A validated CAD score with a demonstrated AUC of 0.80 was applied. Disease prevalence within the study sample was altered by randomly removing non-CAD patients from the original sample. Predictive accuracy and positive predictive value of the CAD score were calculated using 2 × 2 contingency tables. Three threshold values of the CAD score were applied centering on a value for which sensitivity and specificity were equal. RESULTS For a chosen CAD score threshold value (eg, 60), sensitivity (0.74), specificity (0.75), and AUC (0.81) did not change significantly while positive predictive value increased (10%–70%) as disease prevalence increased from 4% to 44%. Changes in predictive accuracy were dependent on the selected test threshold value. Predictive accuracy increased (54%–68%), did not change (74%–75%), or decreased (88%–70%) with the same increase in disease prevalence for threshold values of 50, 60, and 70, respectively. CONCLUSIONS The ROC AUC and predictive accuracy are stable diagnostic characteristics, whereas positive predictive value is greatly influenced by disease prevalence. Discriminatory capabilities of a measurement technique can be assessed by a receiver operating characteristic (ROC) curve analysis (specifically, area under the curve [AUC]) and predictive modeling (predictive accuracy and positive predictive value). We compared the effect of changes in disease prevalence on ROC AUC analysis and predictive modeling.
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No postoperative cardiopulmonary morbidity models have been developed or validated in Chinese patients with lung resection. This study aims to externally validate five predictive models, including Eurolung models, the Brunelli model and the Age-adjusted Charlson Comorbidity Index, in a Chinese population.Patients with lung cancer who underwent anatomic lung resection between 2018/09/01 and 2019/08/31 in our center were involved. Model discrimination was assessed by the area under the receiver operating characteristic curve. Model calibration was evaluated by the Hosmer-Lemeshow test. Calibration curves were plotted. Specificity, sensitivity, negative predictive value, positive predictive value and accuracy were calculated. Model updating was achieved by re-estimating the intercept and/or the slope of the linear predictor and re-estimating all coefficients.Among 1085 patients, 91 patients had postoperative cardiopulmonary complications defined by the European Society of Thoracic Surgeons. For original models, only parsimonious Eurolung1 had acceptable discrimination (area under the receiver operating characteristic curve = 0.688, 95% confidence interval 0.630-0.745) and calibration (p = 0.23 > 0.05) abilities simultaneously. Its sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 0.700, 0.649, 0.153, 0.960 and 0.653, respectively. In the secondary analysis, increased pleural effusion (n = 94), which was nonchylous and nonpurulent, was labeled as a kind of postoperative complication. The area under the receiver operating characteristic curve of the models increased slightly, but all models were miscalibrated. The original Eurolung1 model had the highest discrimination ability but poor calibration, and thus it was updated by three methods. After model updating, new models showed good calibration and small improvements in discrimination. The discrimination ability was still merely acceptable.Overall, none of the models performed well on postoperative cardiopulmonary morbidity prediction in this Chinese population. The original parsimonious Eurolung1 and the updated Eurolung1 were the best-performing models on morbidity prediction, but their discrimination ability only achieved an acceptable level. A multicenter study with more relevant variables and sophisticated statistical methods is warranted to develop new models among Chinese patients in the future.
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