Retrospective Derivation Cohort Research Articles

Accuracy of Devereux and Teichholz formulas for left ventricular mass calculation in different geometric patterns: comparison with cardiac magnetic resonance imaging

Left ventricular (LV) myocardial mass is important in the evaluation of cardiac remodeling and requires accurate assessment when performed on linear measurements in two-dimensional echocardiography (Echo). We aimed to compare the accuracy of the Devereux formula (DEV) and the Teichholz formula (TEICH) in calculating LV myocardial mass in Echo using cardiac magnetic resonance (CMR) as the reference method. Based on preceding mathematical calculations, we identified primarily LV size rather than wall thickness as the main source of bias between DEV and TEICH in a retrospective derivation cohort (n = 1276). Although LV mass from DEV and TEICH were correlated with CMR, TEICH did not show a proportional bias as did DEV (− 2 g/m2 vs. + 22 g/m2). This could be validated in an independent prospective cohort (n = 226) with symptomatic non-ischemic heart failure. DEV systematically overestimated LV mass in all tiers of LV remodeling as compared to TEICH. In conclusion, the TEICH method accounts for the changes in LV geometry with increasing LV mass and thus better reflects the different pattern of LV remodeling than the DEV method. This has important clinical implications, as TEICH may be more appropriate for use in clinical practice, rather than DEV, currently recommended.

Prediction of in-hospital death following acute type A aortic dissection.

Our goal was to create a prediction model for in-hospital death in Chinese patients with acute type A aortic dissection (ATAAD). A retrospective derivation cohort was made up of 340 patients with ATAAD from Tianjin, and the retrospective validation cohort was made up of 153 patients with ATAAD from Nanjing. For variable selection, we used least absolute shrinkage and selection operator analysis, and for risk scoring, we used logistic regression coefficients. We categorized the patients into low-, middle-, and high-risk groups and looked into the correlation with in-hospital fatalities. We established a risk classifier based on independent baseline data using a multivariable logistic model. The prediction performance was determined based on the receiver operating characteristic curve (ROC). Individualized clinical decision-making was conducted by weighing the net benefit in each patient by decision curve analysis (DCA). We created a risk prediction model using risk scores weighted by five preoperatively chosen variables [AUC: 0.7039 (95% CI, 0.643-0.765)]: serum creatinine (Scr), D-dimer, white blood cell (WBC) count, coronary heart disease (CHD), and blood urea nitrogen (BUN). Following that, we categorized the cohort's patients as low-, intermediate-, and high-risk groups. The intermediate- and high-risk groups significantly increased hospital death rates compared to the low-risk group [adjusted OR: 3.973 (95% CI, 1.496-10.552), P < 0.01; 8.280 (95% CI, 3.054-22.448), P < 0.01, respectively). The risk score classifier exhibited better prediction ability than the triple-risk categories classifier [AUC: 0.7039 (95% CI, 0.6425-0.7652) vs. 0.6605 (95% CI, 0.6013-0.7197); P = 0.0022]. The DCA showed relatively good performance for the model in terms of clinical application if the threshold probability in the clinical decision was more than 10%. A risk classifier is an effective strategy for predicting in-hospital death in patients with ATAAD, but it might be affected by the small number of participants.

Simplified Geleijnse score for identifying chest pain features associated with coronary ischemia

BackgroundThe Geleijnse score, which was proposed to assess for coronary ischemia, has practical limitations. ObjectivesOur aim was to design and evaluate a simplified version of the Geleijnse score. MethodsWe enrolled patients with suspected coronary heart disease but negative troponin T or absence of enzymatic curve, and a non-diagnostic 12-lead ECG. The initial study was performed in a retrospective derivation cohort and the results were subsequently validated in a prospective cohort. ResultsFrom 109 patients included in the derivation cohort, 33 (30.3%) received a diagnosis of coronary heart disease. Chest pain with both arms radiation (OR 3.54), severe intensity (OR 2.41), improvement by nitroglycerin (OR 1.61), associated dyspnea (OR 1.97) and prior exertional angina history (OR 2.91) were independently associated with an ischemic origin on multivariate logistic regression analysis. ROC curves comparison demonstrated both the original and simplified scores presented modest predictive ability with significant difference when analyzed using dichotomous cut-offs (0.647 [simplified] vs. 0.544 [original], p = 0.042) but not as a continuous variable (0.670 [simplified] vs. 0.621 [original], p = 0.396). In 305 patients from the validation cohort, the simplified score presented extensively increased predictive accuracy than the Geleijnse, in the continuous (c-indexes = 0.735 vs. 0.685, p = 0.040) and the dichotomic (c-indexes = 0.682 vs. 0.514, p<0.001) forms. ConclusionsA simplified version of the Geleijnse score, including some routine clinical manifestations associated with coronary heart disease, presented significantly better predictive ability compared to the original score.

Role of serum ceruloplasmin in the diagnosis of Wilson's disease: A large Chinese study.

Conventionally, serum ceruloplasmin levels below the lower reference limit (0. 20 g/L) is considered a diagnostic cutoff point for Wilson's disease (WD). However, the lower reference limit varies with assay methodologies and the individuals in the included studies. The objective of this study was to determine the optimal cutoff value of serum ceruloplasmin levels for the diagnosis of WD in a large Chinese cohort and to identify factors associated with serum ceruloplasmin. The cutoff value of ceruloplasmin levels was developed based on a retrospective derivation cohort of 3,548 subjects (1,278 patients with WD and 2,270 controls) and was validated in a separate validation cohort of 313 subjects (203 patients with WD and 110 controls). The performance of immunoassay was tested by receiver operating characteristic curve (ROC) analysis, and differences among the groups were analyzed by using the Mann-Whitney U-test and the Kruskal-Wallis test. The conventional cutoff of serum ceruloplasmin levels of <0.2 g/L had an accuracy of 81.9%, which led to a false-positive rate of 30.5%. The optimal cutoff of the serum ceruloplasmin level for separating patients with WD from other participants was 0.13 g/L, as determined by ROC analysis. This cutoff value had the highest AUC value (0.99), a sensitivity of 97.0%, and a specificity of 96.1%. Moreover, it prevented unnecessary further investigations and treatments for 492 false-positive patients. By determining the correlation between serum ceruloplasmin and phenotypes/genotypes in patients with WD, we found that the serum ceruloplasmin level was lower in early-onset patients and higher in late-onset patients. Interestingly, patients with the R778L/R919G genotype had higher serum ceruloplasmin levels than patients with other hot spot mutation combinations. Our work determined the optimal cutoff value of serum ceruloplasmin levels for the diagnosis of WD and identified differences in serum ceruloplasmin levels with respect to the age of symptom onset and ATP7B mutations, which may provide some valuable insights into the diagnosis and counsel of patients with WD.

Individualised left anterior oblique projection for lead implantation into interventricular septum

ObjectiveWe sought to investigate whether it is possible to obtain individualised left anterior oblique (LAO) by preprocedural electrocardiographic parameters and, if so, whether these parameters can help to improve the...

Prediction of Survival Prognosis for Spinal Metastasis From Cancer of Unknown Primary: Derivation and Validation of a Nomogram Model.

Retrospective and prospective cohort study. Survival estimation is necessary in the decision-making process for treatment in patients with spinal metastasis from cancer of unknown primary (SMCUP). We aimed to develop a novel survival prediction system and compare its accuracy with that of existing survival models. A retrospective derivation cohort of 268 patients and a prospective validation cohort of 105 patients with SMCUP were performed. Univariate and multivariable survival analysis were used to generate independently prognostic variables. A nomogram model for survival prediction was established by integrating these independent predictors based on the size of the significant variables' β regression coefficient. Then, the model was subjected to bootstrap validation with calibration curves and concordance index (C-index). Finally, predictive accuracy was compared with Tomita, revised Tokuhashi and SORG score by the receiver-operating characteristic (ROC) curve. The survival prediction model included six independent prognostic factors, including pathology (P < .001), visceral metastases (P < .001), Frankel score (P < .001), weight loss (P = .005), hemoglobin (P = .001) and serum tumor markers (P < .001). Calibration curve of the model showed good agreement between predicted and actual mortality risk in 6-, 12-, and 24-month estimation in derivation and validation cohorts. The C-index was .775 in the derivation cohort and .771 in the validation cohort. ROC curve analysis showed that the current model had the best accuracy for SMCUP survival estimation amongst 4 models. The novel nomogram system can be applied in survival prediction for SMCUP patients, and furtherly be used to give individualized therapeutic suggestions based on patients' prognosis.

A new method for right ventricular lead implantation into the intraventricular septum

Abstract Funding Acknowledgements Type of funding sources: None. Background Right ventricular (RV) lead placement into the interventricular septum is only accurate in 16–48% of cases under conventional fluoroscopic guidance. A previous report showed the importance of individualized left anterior oblique (LAO) projection to improve the success rate of RV lead placement into the interventricular septum. However, this procedure is complicated. We sought to investigate the preprocedural predictors of individualized LAO projection. Methods We assessed the relationship between preoperative electrocardiographic parameters and the angle of the interventricular septum obtained using thoracic computed tomography (CT). Results In the retrospective derivation cohort group (n=39), the mean angle of individualized LAO assessed by thoracic CT was 53.1±8.9°, and the preoperative electrocardiogram QRS axis was strongly correlated with the interventricular septum axis detected by thoracic CT (R2 = 0.490). In contrast, the preoperative electrocardiographic transitional zone was not associated with the interventricular septum axis (R2 = 0.041, P = 0.78). Using individual LAO projection derived from the preoperative electrocardiogram QRS axis, it was confirmed that the RV lead was placed in the interventricular septum during the pacemaker procedure in the prospective internal validation group (n=30). The success rate for placing the RV lead into the interventricular septum was significantly improved in the internal validation cohort group (93% vs. 64%, P &amp;lt; 0.05). In addition, the N-terminal pro-brain natriuretic peptide level decreased significantly after surgery in the interventricular septal indwelling group. Conclusions Individualized LAO angle derived from the preoperative electrocardiogram QRS axis is a new useful and simple method for RV lead implantation into the interventricular septum.

Incorporation of Laboratory Test Biomarkers Into Dual Antiplatelet Therapy Score Improves Prediction of Ischemic and Bleeding Events in Post-percutaneous Coronary Intervention Patients.

This study aimed to examine the performance of the dual antiplatelet therapy (DAPT) score in two retrospective cohorts of post-percutaneous coronary intervention (PCI) patients and to explore whether incorporating additional biomarkers could further improve the predictive power of the DAPT score. In a retrospective derivation cohort of 4,798 PCI patients, the validity of DAPT score for stratifying ischemic/bleeding risks was explored. Then, the association between the baseline status of 54 laboratory test biomarkers and ischemic/bleeding events was revealed while adjusting for the DAPT score. Combinations of individual laboratory test biomarkers that were significantly associated with ischemic/bleeding events were explored to identify the ones that improved discrimination of ischemic and bleeding events when incorporated into DAPT score. Finally, the impact of the combination of biomarkers with DAPT score was validated in an independent retrospective validation cohort of 1,916 PCI patients. Patients with a high DAPT score (DAPT score ≥ 2) had significantly higher risk of ischemic events and significantly lower risk of bleeding than patients with a low DAPT score (DAPT score < 2). Moreover, the addition of aspartate aminotransferase (AST) and red cell distribution width CV (RDW-CV) into the DAPT score further improved discrimination of ischemia and bleeding. Furthermore, the incremental predictive value of AST + RDW-CV maintained with measurements was updated at post-baseline time points. DAPT score successfully stratified the risks of ischemia/bleeding post PCI in the current cohorts. Incorporation of AST + RDW-CV into the DAPT score further improved prediction for both ischemic and bleeding events.

Early prediction of moderate-to-severe condition of inhalation-induced acute respiratory distress syndrome via interpretable machine learning

BackgroundSeveral studies have investigated the correlation between physiological parameters and the risk of acute respiratory distress syndrome (ARDS), in addition, etiology-associated heterogeneity in ARDS has become an emerging topic quite recently; however, the intersection between the two, which is early prediction of target conditions in etiology-specific ARDS, has not been well-studied. We aimed to develop and validate a machine-learning model for the early prediction of moderate-to-severe condition of inhalation-induced ARDS.MethodsClinical expertise was applied with data-driven analysis. Using data from electronic intensive care units (retrospective derivation cohort) and the three most accessible vital signs (i.e. heart rate, temperature, and respiratory rate) together with feature engineering, we applied a random forest approach during the time window of 90 h that ended 6 h prior to the onset of moderate-to-severe respiratory failure (the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen ≤ 200 mmHg).ResultsThe trained random forest classifier was validated using two independent validation cohorts, with an area under the curve of 0.9127 (95% confidence interval 0.8713–0.9542) and 0.9026 (95% confidence interval 0.8075–1), respectively. A Stable and Interpretable RUle Set (SIRUS) was used to extract rules from the RF to provide guidelines for clinicians. We identified several predictive factors, including resp_96h_6h_min < 9, resp_96h_6h_mean ≥ 16.1, HR_96h_6h_mean ≥ 102, and temp_96h_6h_max > 100, that could be used for predicting inhalation-induced ARDS (moderate-to-severe condition) 6 h prior to onset in critical care units. (‘xxx_96h_6h_min/mean/max’: the minimum/mean/maximum values of the xxx vital sign collected during a 90 h time window beginning 96 h prior to the onset of ARDS and ending 6 h prior to the onset from every recorded blood gas test).ConclusionsThis newly established random forest‑based interpretable model shows good predictive ability for moderate-to-severe inhalation-induced ARDS and may assist clinicians in decision-making, as well as facilitate the enrolment of patients in prevention programmes to improve their outcomes.

A nomogram predicting severe COVID-19 based on a large study cohort from China.

BackgroundThe use of accurate prediction tools and early intervention are important for addressing severe coronavirus disease 2019 (COVID-19). However, the prediction models for severe COVID-19 available to date are subject to various biases. This study aimed to construct a nomogram to provide accurate, personalized predictions of the risk of severe COVID-19.MethodsThis study was based on a large, multicenter retrospective derivation cohort and a validation cohort. The derivation cohort consisted of 496 patients from Jiangsu Province, China, between January 10, 2020, and March 15, 2020, and the validation cohort contained 105 patients from Huangshi, Hunan Province, China, between January 21, 2020, and February 29, 2020. A nomogram was developed with the selected predictors of severe COVID-19, which were identified by univariate and multivariate logistic regression analyses. We evaluated the discrimination of the nomogram with the area under the receiver operating characteristic curve (AUC) and the calibration of the nomogram with calibration plots and Hosmer-Lemeshow tests.ResultsThree predictors, namely, age, lymphocyte count, and pulmonary opacity score, were selected to develop the nomogram. The nomogram exhibited good discrimination (AUC 0.93, 95% confidence interval [CI] 0.90–0.96 in the derivation cohort; AUC 0.85, 95% CI 0.76–0.93 in the validation cohort) and satisfactory agreement.ConclusionsThe nomogram was a reliable tool for assessing the probability of severe COVID-19 and may facilitate clinicians stratifying patients and providing early and optimal therapies.

Development and Validation of a Prognostic Model for One-year Survival of Cirrhosis Patients with First-ever Spontaneous Bacterial Peritonitis.

Background and AimsSpontaneous bacterial peritonitis (SBP) is one of the leading causes of death in patients with liver cirrhosis. We aimed to establish a prognostic model to evaluate the 1-year survival of cirrhosis patients after the first episode of SBP.MethodsA prognostic model was developed based on a retrospective derivation cohort of 309 cirrhosis patients with first-ever SBP and was validated in a separate validation cohort of 141 patients. We used Uno’s concordance, calibration curve, and decision curve (DCA) analysis to evaluate the discrimination, calibration, and clinical net benefit of the model.ResultsA total of 59 (19.1%) patients in the derivation cohort and 42 (29.8%) patients in the validation cohort died over the course of 1 year. A prognostic model in nomogram form was developed with predictors including age [hazard ratio (HR): 1.25; 95% confidence interval (CI): 0.92–1.71], total serum bilirubin (HR: 1.66; 95% CI: 1.28–2.14), serum sodium (HR: 0.94; 95% CI: 0.90–0.98), history of hypertension (HR: 2.52; 95% CI: 1.44–4.41) and hepatic encephalopathy (HR: 2.06; 95% CI: 1.13–3.73). The nomogram had a higher concordance (0.79) compared with the model end-stage liver disease (0.67) or Child-Turcotte-Pugh (0.71) score. The nomogram also showed acceptable calibration (calibration slope, 1.12; Bier score, 0.15±0.21) and optimal clinical net benefit in the validation cohort.ConclusionsThis prediction model developed based on characteristics of first-ever SBP patients may benefit the prediction of patients’ 1-year survival.

Acute Respiratory Distress Syndrome Prediction Score: Derivation and Validation

Despite advances in treatment strategies, acute respiratory distress syndrome (ARDS) after cardiac surgery remains associated with high morbidity and mortality. A method of screening patients for risk of ARDS after cardiac surgery is needed. To develop and validate an ARDS prediction score designed to identify patients at high risk of ARDS after cardiac or aortic surgery. An ARDS prediction score was derived from a retrospective derivation cohort and validated in a prospective cohort. Discrimination and calibration of the score were assessed with area under the receiver operating characteristic curve and the Hosmer-Lemeshow goodness-of-fit test, respectively. A sensitivity analysis was conducted to assess model performance at different cutoff points. The retrospective derivation cohort consisted of 201 patients with and 602 patients without ARDS who had undergone cardiac or aortic surgery. Nine routinely available clinical variables were included in the ARDS prediction score. In the derivation cohort, the score distinguished patients with versus without ARDS with area under the curve of 0.84 (95% CI, 0.81-0.88; Hosmer-Lemeshow P = .55). In the validation cohort, 46 of 1834 patients (2.5%) had ARDS develop within 7 days after cardiac or aortic surgery. Area under the curve was 0.78 (95% CI, 0.71-0.85), and the score was well calibrated (Hosmer-Lemeshow P = .53). The ARDS prediction score can be used to identify high-risk patients from the first day after cardiac or aortic surgery. Patients with a score of 3 or greater should be closely monitored. The score requires external validation before clinical use.

Usefulness of a Novel Electrocardiographic Score to Estimate the Pre-Test Probability of Acute Pulmonary Embolism

According to our experience the 12-lead electrocardiogram (ECG) may be used to estimate the pretest probability of acute pulmonary embolism (acPE). To this end, we devised a novel ECG score (nECGs) composed of 5 known ECG criteria, best characterizing the key pathogenetic steps of acPE. A retrospective derivation cohort including 136 patients with acPE and a prospective validation cohort including 149 consecutive patients were used to devise and validate the nECGs. The latter cohort consisted of 76 patients with acPE and 73 controls presenting with characteristic symptoms of acPE, in whom the work-up ruled out acPE. We compared the diagnostic value of our nECGs with those of another ECG score (Daniel-ECG-score) and of the best prediction rules (3 Wells score and 2 Geneva score variants). The sensitivity (98.7%), negative predictive value (98%), test accuracy (84.4%) and the negative likelihood ratio (LR) (0.019) of the nECGs were superior to those of all other investigated methods. There was no between-groups difference in the positive LR. The specificity (69%) of the nECGs was inferior to those of the Daniel-ECG-score and Wells scores and did not differ or was superior to those of the Geneva score variants. The positive predictive value (77.3%) of the nECGs was superior to those of the 2 Geneva scores and did not differ from those of the other methods. In conclusion, the nECGs due to its superior sensitivity, negative predictive value, test accuracy, and negative LR estimated the pretest probability of acPE better than the Daniel-ECG-score and the prediction rules.

Thermodilution-derived temperature recovery time: a novel predictor of microvascular reperfusion and prognosis after myocardial infarction.

Novel parameters that detect failed microvascular reperfusion might identify better the patients likely to benefit from adjunctive treatments during primary percutaneous coronary intervention (PCI). The aim of this study was to test the hypothesis that a novel invasive parameter, the thermodilution-derived temperature recovery time (TRT), would be associated with microvascular obstruction (MVO) and prognosis. TRT was derived and validated in two independent ST-elevation myocardial infarction populations and was measured immediately post PCI. TRT was defined as the duration (seconds) from the nadir of the hyperaemic thermodilution curve to 20% from baseline body temperature. MVO extent (% left ventricular mass) was assessed by cardiovascular magnetic resonance imaging at 2-7 days. In the retrospective derivation cohort (n=271, mean age 60±12 years, 72% male), higher TRT was associated with more MVO (coefficient: 4.09 [95% CI: 2.70-5.48], p<0.001), independently of IMR >32, CFR ≤2, hyperaemic Tmn >median, thermodilution waveform, age and ischaemic time. At five years, higher TRT was multivariably associated with all-cause death/heart failure hospitalisation (OR 4.14 [95% CI: 2.08-8.25], p<0.001) and major adverse cardiac events (OR 4.05 [95% CI: 2.00-8.21], p<0.001). In the validation population (n=144, mean age 59±11 years, 80% male), the findings were confirmed prospectively. TRT represents a novel diagnostic advance for predicting MVO and prognosis. ClinicalTrials.gov Identifiers: NCT02072850 & NCT02257294 Visual summary. Thermodilution-derived temperature recovery time (TRT): a novel predictor of microvascular reperfusion & prognosis after STEMI. CMR: cardiovascular magnetic resonance; MACE: major adverse cardiac events; MVO: microvascular obstruction; PCI: percutaneous coronary intervention; STEMI: ST-segment elevation myocardial infarction.

Left Atrial Volume as a Biomarker of Atrial Fibrillation at Routine Chest CT: Deep Learning Approach.

To test the performance of a deep learning (DL) model in predicting atrial fibrillation (AF) at routine nongated chest CT. A retrospective derivation cohort (mean age, 64 years; 51% female) consisting of 500 consecutive patients who underwent routine chest CT served as the training set for a DL model that was used to measure left atrial volume. The model was then used to measure atrial size for a separate 500-patient validation cohort (mean age, 61 years; 46% female), in which the AF status was determined by performing a chart review. The performance of automated atrial size as a predictor of AF was evaluated by using a receiver operating characteristic analysis. There was good agreement between manual and model-generated segmentation maps by all measures of overlap and surface distance (mean Dice = 0.87, intersection over union = 0.77, Hausdorff distance = 4.36 mm, average symmetric surface distance = 0.96 mm), and agreement was slightly but significantly greater than that between human observers (mean Dice = 0.85 [automated] vs 0.84 [manual]; P = .004). Atrial volume was a good predictor of AF in the validation cohort (area under the receiver operating characteristic curve = 0.768) and was an independent predictor of AF, with an age-adjusted relative risk of 2.9. Left atrial volume is an independent predictor of the AF status as measured at routine nongated chest CT. Deep learning is a suitable tool for automated measurement.© RSNA, 2019See also the commentary by de Roos and Tao in this issue.

200. Development and validation of a radiographic classification of lumbar lateral listhesis for predicting radicular leg pain in adult scoliosis

BACKGROUND CONTEXT Lumbar lateral listhesis has been found to be associated with radicular leg pain in adult scoliosis. This relationship, however, has yet to be specified by making a simple grading/classification system of lateral listhesis that stratifies the risk of leg pain. Such a classification could better alert surgeons of potential involvement of nerve impingement early in the evaluation, especially in such emerging settings as “telehealth wherein history inquiry and image review may take place separately. PURPOSE To develop and validate a radiographic classification system of lumbar lateral listhesis that stratifies the probability of having radicular leg pain in adult scoliosis. STUDY DESIGN/SETTING Derivation and validation of a risk-based classification system, at a tertiary university hospital. PATIENT SAMPLE A total of 294 consecutive adult scoliosis patients enrolled in a retrospective derivation cohort (189 patients) and a prospective validation cohort (105 patients). OUTCOME MEASURES Odds ratio of the radiological parameters of lumbar lateral listhesis in predicting leg pain, and the percentage of patients with leg pain in each type of lateral listhesis. METHODS A radiographic classification of lumbar lateral listhesis was developed using a derivation (retrospective) cohort of consecutive adult scoliosis patients diagnosed at our clinic in 2011-2015. Four radiographic factors of lateral listhesis were put into multivariable logistic regression to identify two most influential predictors of radicular leg pain:(1) L4 lateral listhesis, (2) Nash-and-Moe grade of vertebral rotation at the lowest slipped segment, (3) the number of slipped segments, and (4) the presence of another listhesis superiorly at the thoracolumbar junction contralateral to the main listhesis below. . These four factors were selected based on research evidence and clinical experience, and odds ratio was used to assess their strength of association with the presence of radicular leg pain. The classification was thus made based on the two most influential factors of the four and, then, validated in a prospective cohort of consecutive patients diagnosed in 2016-2018. RESULTS In the derivation cohort of 189 patients, vertebral rotation ≥ grade 2 (OR=9.5, 95% CI: 3.8-24.6) and the presence of L4 lateral listhesis (OR=4.5, 95% CI: 1.8-12.1) were found to be most strongly associated with the presence of radicular leg pain. The classification of lumbar lateral listhesis was thereby made based on the presence of these two factors: Type 1 (neither of the two factors present), Type 2 (one of two factors present), and Type 3 (both factors present). Additionally, Type 0 was used to describe scoliosis without any lateral listhesis. The prevalence of leg pain varied significantly among the four types: 0% (0/49), 6% (3/47), 34% (25/74), and 68% (13/19) in Type 0, 1, 2, and 3, respectively (P CONCLUSIONS This classification system of lumbar lateral listhesis can effectively stratify the probability of radicular leg pain in adult scoliosis. Type 2 and Type 3 lateral listhesis should alert physicians of potential nerve impingement early in the evaluation. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

A modified acute respiratory distress syndrome prediction score: a multicenter cohort study in China.

Early recognition of the risks of acute respiratory distress syndrome (ARDS) and prevention of the development of ARDS may be more effective in improving patient outcomes. We performed the present study to determine the ARDS risk factors in a Chinese population and validate a score to predict the development of ARDS. This was an observational multicenter cohort study performed in 13 tertiary hospitals in China. Patients admitted into participating intensive care units (ICUs) from January 1 to January 31, 2012, and from January 1 to January 10, 2013, were enrolled in a retrospective derivation cohort and a prospective validation cohort, respectively. In the derivation cohort, the potential risk factors of ARDS were collected. The confirmed risk factors were determined with univariate and multivariate logistic regression analyses, and then the modified ARDS prediction score (MAPS) was established. We prospectively enrolled patients to verify the accuracy of MAPS. A total of 479 and 198 patients were enrolled into the retrospective derivation cohort and the prospective validation cohort, respectively. A total of 93 (19.4%) patients developed ARDS in the derivation cohort. Acute pancreatitis, pneumonia, hypoalbuminemia, acidosis, and high respiratory rate were the risk factors for ARDS. The MAPS discriminated patients who developed ARDS from those who did not, with an area under the curve (AUC) of 0.809 [95% confidence interval (CI), 0.758-0.859, P<0.001]. In the prospective validation cohort, performance of the MAPS was similar to the retrospective derivation cohort, with an AUC of 0.792 (95% CI, 0.717-0.867, P<0.001). The lung injury prediction score (LIPS) showed a predicted value of an AUC of 0.770 (95% CI, 0.728-0.812, P<0.001) in our patients, which was significantly lower than our score (P<0.046). The MAPS based on risk factors could help the clinician to predict patients who will develop ARDS. ClinicalTrials.gov NCT01666834.

Modelling and predicting drowning risk along the Gironde's Oceanic Coast: Retrospective, prospective observational and pilot experimental studies

Introduction In France, a national study about drowning is conducted every three years to register all cases of drowning leading to a hospitalization or death, from June 1st until September 15th. In 2015, 596 drowning occurred on seashore, including 38 drowning implying 7 deaths on beaches of Gironde, Southwestern France. Gironde's oceanic coast is a 126-km-long sand beach; the main touristic areas are watched during summer, but bathing season can begin in April and last till mid-October. Most drowning are related to rip currents, shown by Castelle et al. to be controlled by wave conditions, tide levels and local topography. To orient prevention and surveillance strategies, our study aimed to create a risk prediction model of drowning along Gironde's coast, based on weather and marine forecasts. Methods Retrospective derivation cohort data were collected from the emergency call centre of Gironde, and meteorological data came from MeteoFrance, the national weather forecaster. The study period was April, 1st till October, 31st, from 2011 till 2013. Prospective validation cohort data and forecasts up to three days were collected from the same sources, for the same months in 2015 and 2016. We used a logistic regression and a zero-inflated Poisson model to quantify drowning risk. Variables selection was done on the retrospective cohort, and we assigned for each model a threshold to stratify the risk of drowning (“low” and “high”). We finally tested the prediction model by comparing prediction and lifeguard assessment of the drowning risk for each day during the summer 2017. As a pilot study, only one lifeguard post, from Le Porge, participated. We calculated Cohen's Kappa initially with the binary model then with a 5-stage scale using the zero-inflated model. Results The retrospective sample included 117 days with at least one drowning event (272 events reported), within a total of 546 days without unavailable data. Air and water temperatures, wave factor, wave direction, nebulosity and holidays were positively associated with drowning probability. Prospective validation was performed on 405 days without missing data, covering 181 drowning reported during 80 different days. The regression logistic model had an area under the ROC curve (AUC) of 0.83 (95% confidence interval: [0.79 − 0.87]). With 22 events missed in 13 days, the predictive positive value (PPV) was 40%, and the negative predictive value (NPV) 94%. The zero-inflated Poisson model had an AUC of 0.85 ([0.83 − 0.91], PPV: 37%, NPV: 97%) and missed 14 drowning events in 7 days. Within those 14 events, 5 occurred during extreme wave conditions, 1 was consecutive to seizure, 1 was a 4-year-old and 1 was a body-boarder. The model missed a day with 5 people rescued in the same rip current, without any injury. There were no statistical differences between the 1-, 2- and 3-day forecasts. The test phase has lasted 86 days, with 62 (72%) without missing data. The binary model was tested during 48 days, with a Cohen's kappa at 0.57 (moderate agreement). It was found inappropriate by lifeguarders as they needed intermediate risk stage. The 5-stage scale was then tested during the last 14 days, with a Cohen's kappa at 0.77 (strong agreement). Conclusion Our models show that environmental conditions are good predictors of drowning risk along the Gironde's oceanic coast. They could help prevent drowning by broadcasting warning messages up to three days before a day at risk. Lifeguards confirmed the relevance of the models at a local scale, and we introduced a 5-stage risk scale. Further study is needed to collect significant data. A more detailed study is conducted to predict the risk depending on the hours of the days and the tide level, which could be more informative for beach patrols.

Derivation and validation of E/e′ ratio as a parameter in the evaluation of left atrial appendage thrombus formation in patients with nonvalvular atrial fibrillation

We sought to determine and prospectively validate, with concomitantly performed transthoracic (TTE) and transesophageal echocardiograms (TEE), a TTE-assessed E/e' threshold that can be useful in predicting left atrial appendage (LAA) thrombus in patients with nonvalvular atrial fibrillation (NVAF). The retrospective derivation cohort was comprised of 297 patients with NVAF with TTE performed within 1year of TEE. The validation cohort was comprised of 266 prospectively enrolled patients with TTE performed immediately prior to TEE. LAA thrombus was detected by TEE in 6.4 % of patients in both cohorts. Receiver operating characteristic (ROC) analyses demonstrated a good discriminatory capacity of lateral E/e' in predicting LAA thrombus in the derivation cohort (AUC 0.72; CI 0.63-0.82; P = 0.001) which was confirmed in the validation cohort (AUC 0.83; CI 0.75-0.91; P < 0.001). In the derivation cohort, ROC curve point-coordinates identified E/e' thresholds of both 9.0 and 8.0 to be associated with 100 % sensitivity, with specificities of 36 and 30 %, respectively. An E/e' threshold of ≥8 was selected a priori for prospective validation, and was associated with 100 % sensitivity and 41 % specificity for LAA thrombus, with positive and negative predictive values of 10 and 100 %, respectively, and positive and negative likelihood ratios of 1.69 and 0, respectively. We determined and validated an E/e' threshold of 8 as a highly sensitive and useful parameter that can aid in identifying patients at very low risk for LAA thrombus and potentially obviate the need for a TEE prior to electrophysiology procedures and restoration of sinus rhythm.

From statistical significance to clinical relevance: A simple algorithm to integrate brain natriuretic peptide and the Seattle Heart Failure Model for risk stratification in heart failure

Heart failure (HF) guidelines recommend brain natriuretic peptide (BNP) and multivariable risk scores, such as the Seattle Heart Failure Model (SHFM), to predict risk in HF with reduced ejection fraction (HFrEF). A practical way to integrate information from these 2 prognostic tools is lacking. We sought to establish a SHFM+BNP risk-stratification algorithm. The retrospective derivation cohort included consecutive patients with HFrEF at the Mayo Clinic. One-year outcome (death, transplantation or ventricular assist device) was assessed. The SHFM+BNP algorithm was derived by stratifying patients within SHFM-predicted risk categories (≤2.5%, 2.6% to ≤10%, >10%) according to BNP above or below 700 pg/ml and comparing SHFM-predicted and observed event rates within each SHFM+BNP category. The algorithm was validated in a prospective, multicenter HFrEF registry (Penn HF Study). Derivation (n = 441; 1-year event rate 17%) and validation (n = 1,513; 1-year event rate 12%) cohorts differed with the former being older and more likely ischemic with worse symptoms, lower EF, worse renal function and higher BNP and SHFM scores. In both cohorts, across the 3 SHFM-predicted risk strata, a BNP >700 pg/ml consistently identified patients with approximately 3-fold the risk that the SHFM would have otherwise estimated, regardless of stage of HF, intensity and duration of HF therapy and comorbidities. Conversely, the SHFM was appropriately calibrated in patients with a BNP <700 pg/ml. The simple SHFM+BNP algorithm displays stable performance across diverse HFrEF cohorts and may enhance risk stratification to enable appropriate decision-making regarding HF therapeutic or palliative strategies.