Erratum to Palareti et al. “The SAME-TT2R2 score predicts the quality of anticoagulation control in patients with acute VTE. A real-life inception cohort study” (Thromb Haemost 2016; 115: 1101-1108)

2016 ◽  
Vol 116 (08) ◽  
pp. 396-396
Keyword(s):  
Cohort Study ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Real Life ◽  
Inception Cohort ◽  
C Statistic ◽  
Anticoagulation Control

In the Original Article “The SAME-TT2R2 score predicts the quality of anticoagulation control in patients with acute VTE. A real-life inception cohort study” (Thromb Haemost 2016; 115: 1101-1108) by Palareti et al. the last sentence at the end of the second paragraph on page 1105 of the article is wrong and should read as follows: “Regarding the ability of the score ≥2 to predict a TTR <65%, the C statistic was 0.52 (95% CI 0.48–0.55; p = 0.35); the sensitivity was 74% (70.4–77.8), specificity 29% (25.3–33.2), negative predictive value 52% (95% CI 45.8–57.7) and positive predictive value 53% (40.0–56.2).” The authors apologise for this error.

scholarly journals Highly sensitive scent-detection of COVID-19 patients in vivo by trained dogs

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257474
Author(s):  
Omar Vesga ◽  
Maria Agudelo ◽  
Andrés F. Valencia-Jaramillo ◽  
Alejandro Mira-Montoya ◽  
Felipe Ossa-Ospina ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Limit Of Detection ◽  
Real Life ◽  
The Body ◽  
In Vivo Efficacy ◽  
Scent Detection

Timely and accurate diagnostics are essential to fight the COVID-19 pandemic, but no test satisfies both conditions. Dogs can scent-identify the unique odors of volatile organic compounds generated during infection by interrogating specimens or, ideally, the body of a patient. After training 6 dogs to detect SARS-CoV-2 by scent in human respiratory secretions (in vitro diagnosis), we retrained 5 of them to search and find the infection by scenting the patient directly (in vivo screening). Then, efficacy trials were designed to compare the diagnostic performance of the dogs against that of the rRT-PCR in 848 human subjects: 269 hospitalized patients (COVID-19 prevalence 30.1%), 259 hospital staff (prevalence 2.7%), and 320 government employees (prevalence 1.25%). The limit of detection in vitro was lower than 10−12 copies ssRNA/mL. During in vivo efficacy experiments, our 5 dogs detected 92 COVID-19 positive patients among the 848 study subjects. The alert (lying down) was immediate, with 95.2% accuracy and high sensitivity (95.9%; 95% C.I. 93.6–97.4), specificity (95.1%; 94.4–95.8), positive predictive value (69.7%; 65.9–73.2), and negative predictive value (99.5%; 99.2–99.7) in relation to rRT-PCR. Seventy-five days after finishing in vivo efficacy experiments, a real-life study (in vivo effectiveness) was executed among the riders of the Metro System of Medellin, deploying the human-canine teams without previous training or announcement. Three dogs were used to examine the scent of 550 volunteers who agreed to participate, both in test with canines and in rRT-PCR testing. Negative predictive value remained at 99.0% (95% C.I. 98.3–99.4), but positive predictive value dropped to 28.2% (95% C.I. 21.1–36.7). Canine scent-detection in vivo is a highly accurate screening test for COVID-19, and it detects more than 99% of infected individuals independent of key variables, such as disease prevalence, time post-exposure, or presence of symptoms. Additional training is required to teach the dogs to ignore odoriferous contamination under real-life conditions.

Pre-operative prediction of cholesteatomas from radiology: retrospective cohort study of 106 cases

2019 ◽  
Vol 133 (06) ◽  
pp. 477-481 ◽  
Author(s):  
D Selwyn ◽  
J Howard ◽  
P Cuddihy
Keyword(s):  
Computed Tomography ◽  
Cohort Study ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Retrospective Cohort Study ◽  
Predictive Value ◽  
Retrospective Cohort ◽  
Anatomical Variation ◽  
Predictive Values ◽  
Mastoid Surgery

AbstractObjectivePre-operative imaging is often used to predict the extent of a cholesteatoma and anatomical variation to plan for surgery. This study aimed to measure the predictive accuracy of computed tomography findings.MethodsA retrospective cohort study was conducted of all patients in a district general hospital undergoing mastoid surgery within a consecutive 12-month period, in whom computed tomography had been performed prior to operative intervention. The study measured the key findings of pre-operative computed tomography imaging and compared them to the intra-operative findings.ResultsA total of 106 patients were included. The sensitivity and specificity for predicting cholesteatoma were 79 per cent and 81 per cent respectively. The positive predictive value was 90 per cent and the negative predictive value was 65 per cent. In predicting complications of cholesteatomas, the sensitivity was 70 per cent, whereas the specificity was 91 per cent. The positive predictive value was 88 per cent and the negative predictive value was 76 per cent.ConclusionPre-operative computed tomography conducted prior to mastoid surgery has high positive predictive values for both predicting cholesteatomas and complications (90 per cent and 88 per cent respectively).

Predictive Value of Matrix Metalloproteinases and Their Inhibitors for Mortality in Septic Patients: A Cohort Study

2017 ◽  
Vol 35 (1) ◽  
pp. 95-103 ◽  
Author(s):  
Sergio Serrano-Gomez ◽  
Gabriel Burgos-Angulo ◽  
Daniela Camila Niño-Vargas ◽  
María Eugenia Niño ◽  
María Eugenia Cárdenas ◽  
...  
Keyword(s):  
Cohort Study ◽  
Matrix Metalloproteinases ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Characteristic Curve ◽  
Population Sample ◽  
Tissue Inhibitor Of Metalloproteinase ◽  
Predictive Values ◽  
Sensitivity Specificity

Purpose: Over 170 biomarkers are being investigated regarding their prognostic and diagnostic accuracy in sepsis in order to find new tools to reduce morbidity and mortality. Matrix metalloproteinases (MMPs) and their inhibitors have been recently studied as promising new prognostic biomarkers in patients with sepsis. This study is aimed at determining the utility of several cutoff points of these biomarkers to predict mortality in patients with sepsis. Materials and Methods: A multicenter, prospective, analytic cohort study was performed in the metropolitan area of Bucaramanga, Colombia. A total of 289 patients with sepsis and septic shock were included. MMP-9, MMP-2, tissue inhibitor of metalloproteinase 1 (TIMP-1), TIMP-2, TIMP-1/MMP-9 ratio, and TIMP-2/MMP-2 ratio were determined in blood samples. Value ranges were correlated with mortality to estimate sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiving operating characteristic curve. Results: Sensitivity ranged from 33.3% (MMP-9/TIMP-1 ratio) to 60.6% (TIMP-1) and specificity varied from 38.8% (MMP-2/TIMP-2 ratio) to 58.5% (TIMP-1). As for predictive values, positive predictive value range was from 17.5% (MMP-9/TIMP-1 ratio) to 70.4% (MMP-2/TIMP-2 ratio), whereas negative predictive values were between 23.2% (MMP-2/TIMP-2 ratio) and 80.9% (TIMP-1). Finally, area under the curve scores ranged from 0.31 (MMP-9/TIMP-1 ratio) to 0.623 (TIMP-1). Conclusion: Although TIMP-1 showed higher sensitivity, specificity, and negative predictive value, with a representative population sample, we conclude that none of the evaluated biomarkers had significant predictive value for mortality.

scholarly journals Hospitalization Risk During Chemotherapy for Advanced Cancer: Development and Validation of Risk Stratification Models Using Real-World Data

2019 ◽  
pp. 1-10
Author(s):  
Gabriel A. Brooks ◽  
Hajime Uno ◽  
Erin J. Aiello Bowles ◽  
Alexander R. Menter ◽  
Maureen O’Keeffe-Rosetti ◽  
...  
Keyword(s):  
High Risk ◽  
Positive Predictive Value ◽  
Risk Stratification ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Validation Cohort ◽  
Risk Group ◽  
Health Plans ◽  
C Statistic ◽  
Chemotherapy Initiation

PURPOSE Hospitalizations are a common occurrence during chemotherapy for advanced cancer. Validated risk stratification tools could facilitate proactive approaches for reducing hospitalizations by identifying at-risk patients. PATIENTS AND METHODS We assembled two retrospective cohorts of patients receiving chemotherapy for advanced nonhematologic cancer; cohorts were drawn from three integrated health plans of the Cancer Research Network. We used these cohorts to develop and validate logistic regression models estimating 30-day hospitalization risk after chemotherapy initiation. The development cohort included patients in two health plans from 2005 to 2013. The validation cohort included patients in a third health plan from 2007 to 2016. Candidate predictor variables were derived from clinical data in institutional data warehouses. Models were validated based on the C-statistic, positive predictive value, and negative predictive value. Positive predictive value and negative predictive value were calculated in reference to a prespecified risk threshold (hospitalization risk ≥ 18.0%). RESULTS There were 3,606 patients in the development cohort (median age, 63 years) and 634 evaluable patients in the validation cohort (median age, 64 years). Lung cancer was the most common diagnosis in both cohorts (26% and 31%, respectively). The selected risk stratification model included two variables: albumin and sodium. The model C-statistic in the validation cohort was 0.69 (95% CI, 0.62 to 0.75); 39% of patients were classified as high risk according to the prespecified threshold; 30-day hospitalization risk was 24.2% (95% CI, 19.9% to 32.0%) in the high-risk group and 8.7% (95% CI, 6.1% to 12.0%) in the low-risk group. CONCLUSION A model based on data elements routinely collected during cancer treatment can reliably identify patients at high risk for hospitalization after chemotherapy initiation. Additional research is necessary to determine whether this model can be deployed to prevent chemotherapy-related hospitalizations.

scholarly journals Highly sensitive scent-detection of COVID-19 patients in vivo by trained dogs

2021 ◽  
Author(s):  
Omar Vesga ◽  
Maria Agudelo ◽  
Andres F Valencia-Jaramillo ◽  
Alejandro Mira-Montoya ◽  
Ivan Felipe Ossa-Ospina ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Limit Of Detection ◽  
Human Subjects ◽  
Real Life ◽  
The Body ◽  
Scent Detection

Timely and accurate diagnostics are essential to fight the COVID-19 pandemic, but no test satisfies both conditions. Dogs can scent-identify the unique odors of the volatile organic compounds generated during infection by interrogating specimens or, ideally, the body of a patient. After training 6 dogs to detect SARS-CoV-2 in human respiratory secretions (in vitro scent-detection), we retrained 5 of them to diagnose the infection by scenting the patient directly (in vivo scent-detection). Then, efficacy trials were designed to compare the diagnostic performance of the dogs against that of the rRT-PCR in 848 human subjects: 269 hospitalized patients (COVID-19 prevalence 30.1%), 259 hospital staff (prevalence 2.7%), and 320 government employees (prevalence 1.25%). The limit of detection in vitro was lower than 10-12 copies ssRNA/mL. In vivo, all dogs detected 92 COVID-19 patients present among the 848 study subjects. Detection was immediate, and independent of prevalence, time post-exposure, or presence of symptoms, with 95.2% accuracy and high sensitivity (95.9%; 95% C.I. 93.6-97.4), specificity (95.1%; 94.4-95.8), positive predictive value (69.7%; 65.9-73.2), and negative predictive value (99.5%; 99.2-99.7). To determine real-life performance, we waited 75 days to carry out an effectiveness assay among the riders of the Metro System of Medellin, deploying the human-canine teams without previous training or announcement. Three dogs (one of each breed) scent-interrogated 550 citizens who volunteered for simultaneous canine and rRT-PCR testing. Negative predictive value remained at 99.0% (95% C.I. 98.3-99.4), but positive predictive value dropped to 28.2% (95% C.I. 21.1-36.7). Canine scent-detection in vivo is a highly accurate screening test for COVID-19, and it detects more than 99% of infected individuals independently of the key variables. However, real-life conditions increased substantially the number of false positives, indicating the necessity of training a threshold for the limit of detection to discriminate environmental odoriferous contamination from infection.

scholarly journals Structure, Process, and Outcome Quality of Surgical Site Infection Surveillance in Switzerland

2017 ◽  
Vol 38 (10) ◽  
pp. 1172-1181 ◽  
Author(s):  
Stefan P. Kuster ◽  
Marie-Christine Eisenring ◽  
Hugo Sax ◽  
Nicolas Troillet ◽  
Keyword(s):  
Positive Predictive Value ◽  
Surgical Site Infection ◽  
Negative Predictive Value ◽  
Random Sample ◽  
Predictive Value ◽  
Chart Review ◽  
Patient Records ◽  
Site Infection ◽  
Validation Score

OBJECTIVETo assess the structure and quality of surveillance activities and to validate outcome detection in the Swiss national surgical site infection (SSI) surveillance program.DESIGNCountrywide survey of SSI surveillance quality.SETTING147 hospitals or hospital units with surgical activities in Switzerland.METHODSSite visits were conducted with on-site structured interviews and review of a random sample of 15 patient records per hospital: 10 from the entire data set and 5 from a subset of patients with originally reported infection. Process and structure were rated in 9 domains with a weighted overall validation score, and sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the identification of SSI.RESULTSOf 50 possible points, the median validation score was 35.5 (range, 16.25–48.5). Public hospitals (P<.001), hospitals in the Italian-speaking region of Switzerland (P=.021), and hospitals with longer participation in the surveillance (P=.018) had higher scores than others. Domains that contributed most to lower scores were quality of chart review and quality of data extraction. Of 49 infections, 15 (30.6%) had been overlooked in a random sample of 1,110 patient records, accounting for a sensitivity of 69.4% (95% confidence interval [CI], 54.6%–81.7%), a specificity of 99.9% (95% CI, 99.5%–100%), a positive predictive value of 97.1% (95% CI, 85.1%–99.9%), and a negative predictive value of 98.6% (95% CI, 97.7%–99.2%).CONCLUSIONSIrrespective of a well-defined surveillance methodology, there is a wide variation of SSI surveillance quality. The quality of chart review and the accuracy of data collection are the main areas for improvement.Infect Control Hosp Epidemiol 2017;38:1172–1181

scholarly journals The Value of the Surprise Question to Predict One-Year Mortality in Idiopathic Pulmonary Fibrosis: A Prospective Cohort Study

Respiration ◽  
2021 ◽  
pp. 1-6
Author(s):  
Catharina C. Moor ◽  
Nelleke C. Tak van Jaarsveld ◽  
Catherine Owusuaa ◽  
Jelle R. Miedema ◽  
Sara Baart ◽  
...  
Keyword(s):  
Palliative Care ◽  
Cohort Study ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Prospective Cohort ◽  
C Statistic ◽  
One Year ◽  
Surprise Question

<b><i>Background:</i></b> Idiopathic pulmonary fibrosis (IPF) is a progressive fatal disease with a heterogeneous disease course. Timely initiation of palliative care is often lacking. The surprise question “Would you be surprised if this patient died within the next year?” is increasingly used as a clinical prognostic tool in chronic diseases but has never been evaluated in IPF. <b><i>Objective:</i></b> We aimed to evaluate the predictive value of the surprise question for 1-year mortality in IPF. <b><i>Methods:</i></b> In this prospective cohort study, clinicians answered the surprise question for each included patient. Clinical parameters and mortality data were collected. The sensitivity, specificity, accuracy, negative, and positive predictive value of the surprise question with regard to 1-year mortality were calculated. Multivariable logistic regression analysis was performed to evaluate which factors were associated with mortality. In addition, discriminative performance of the surprise question was assessed using the C-statistic. <b><i>Results:</i></b> In total, 140 patients were included. One-year all-cause mortality was 20% (<i>n</i> = 28). Clinicians identified patients with a survival of &#x3c;1 year with a sensitivity of 68%, a specificity of 82%, an accuracy of 79%, a positive predictive value of 49%, and a negative predictive value of 91%. The surprise question significantly predicted 1-year mortality in a multivariable model (OR 3.69; 95% CI 1.24–11.02; <i>p</i> = 0.019). The C-statistic of the surprise question to predict mortality was 0.75 (95% CI 0.66–0.85). <b><i>Conclusions:</i></b> The answer on the surprise question can accurately predict 1-year mortality in IPF. Hence, this simple tool may enable timely focus on palliative care for patients with IPF.

scholarly journals Validity of the FINDRISC as a prediction tool for diabetes in a contemporary Norwegian population: a 10-year follow-up of the HUNT study

2019 ◽  
Vol 7 (1) ◽  
pp. e000769 ◽  
Author(s):  
Anne Jølle ◽  
Kristian Midthjell ◽  
Jostein Holmen ◽  
Sven Magnus Carlsen ◽  
Jaakko Tuomilehto ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Adult Population ◽  
Diabetes Incidence ◽  
Prescription Database ◽  
Entire Study ◽  
Glucose Lowering ◽  
C Statistic ◽  
Norwegian Population

ObjectiveThe Finnish Diabetes Risk Score (FINDRISC) is a recommended tool for type 2 diabetes prediction. There is a lack of studies examining the performance of the current 0–26 point FINDRISC scale. We examined the validity of FINDRISC in a contemporary Norwegian risk environment.Research design and methodsWe followed 47 804 participants without known diabetes and aged ≥20 years in the HUNT3 survey (2006–2008) by linkage to information on glucose-lowering drug dispensing in the Norwegian Prescription Database (2004–2016). We estimated the C-statistic, sensitivity and specificity of FINDRISC as predictor of incident diabetes, as indicated by incident use of glucose-lowering drugs. We estimated the 10-year cumulative diabetes incidence by categories of FINDRISC.ResultsThe C-statistic (95% CI) of FINDRISC in predicting future diabetes was 0.77 (0.76 to 0.78). FINDRISC ≥15 (the conventional cut-off value) had a sensitivity of 38% and a specificity of 90%. The 10-year cumulative diabetes incidence (95% CI) was 4.0% (3.8% to 4.2%) in the entire study population, 13.5% (12.5% to 14.5%) for people with FINDRISC ≥15 and 2.8% (2.6% to 3.0%) for people with FINDRISC <15. Thus, FINDRISC ≥15 had a positive predictive value of 13.5% and a negative predictive value of 97.2% for diabetes within the next 10 years. To approach a similar sensitivity as in the study in which FINDRISC was developed, we would have to lower the cut-off value for elevated FINDRISC to ≥11. This would yield a sensitivity of 73%, specificity of 67%, positive predictive value of 7.7% and negative predictive value of 98.5%.ConclusionsThe validity of FINDRISC and the risk of diabetes among people with FINDRISC ≥15 is substantially lower in the contemporary Norwegian population than assumed in official guidelines. To identify ~3/4 of those developing diabetes within the next 10 years, we would have to lower the threshold for elevated FINDRISC to ≥11, which would label ~1/3 of the entire adult population as having an elevated FINDRISC necessitating a glycemia assessment.

scholarly journals The comparison of predictive value of cervical length in singleton spontaneous preterm labor with in vitro fertilization pregnancies: A cohort study

2021 ◽  
Author(s):  
Razieh Mohammad ◽  
Mahvash Zargar ◽  
Mojgan Barati ◽  
Somayeh Ershadian
Keyword(s):  
Cohort Study ◽  
In Vitro Fertilization ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Preterm Labor ◽  
Roc Analysis ◽  
Cervical Length ◽  
Vitro Fertilization

Background: Preterm labor (PTL) is one of the most important factors in neonatal mortality. Some studies have revealed a reverse relationship between cervical length (CL) and PTL, however, further evidence is needed to confirm it. Objective: To investigate the predictive value of CL in spontaneous and in vitro fertilization (IVF) pregnancies. Materials and Methods: This prospective cohort study was performed on 154 pregnant women from 16-26 wk of gestation with a singleton fetus in spontaneous delivery (n = 77) and IVF pregnancies (n = 77) and followed up until delivery. Women with multiple pregnancy, placenta previa, cerclage, and congenital anomalies were excluded from the study. The cut-off determination was done according to the Roc analysis. Results: The mean CL in term delivery and PTL groups were 37 ± 7 mm and 31 ± 6 mm, respectively (p < 0.001). The frequency of PTL in spontaneous and IVF pregnancies were 7.8% and 23.27%, respectively (p = 0.007). According to the Roc analysis, the best cut-off for normal pregnancy was ≤ 36 mm with the negative predictive value of 97.9%, the positive predictive value of 11.4%, sensitivity 83.3%, and specificity of 46.5%. While for the IVF group, the cut off was ≤ 30 mm, with a negative predictive value of 88.4%, a positive predictive value of 57.8%, sensitivity of 63.2%, and a specificity of 86%. Conclusion: In this study, IVF had a significant direct correlation with PTL. CL also had a significant indirect relationship with PTL. Key words: Cervix, IVF, Preterm delivery, PTL.

SAMe-TT₂R₂ Score for Prediction of Anticoagulation Control in Thai Patients with Non-Valvular Atrial Fibrillation

2021 ◽  
Vol 104 (6) ◽  
pp. 959-963
Keyword(s):  
Atrial Fibrillation ◽  
Positive Predictive Value ◽  
Sensitivity And Specificity ◽  
Predictive Value ◽  
International Normalized Ratio ◽  
Control Objective ◽  
Anticoagulation Control ◽  
Sensitivity Specificity

Background: The quality of anticoagulation control is an important determination of thromboembolism and bleeding in patients with non-valvular atrial fibrillation. Previous trials have shown that SAMe-TT₂R₂ score could be used for prediction of anticoagulation control. Objective: To predict labile international normalized ratio (INR) by SAMe-TT₂R₂ score in Thai patients with non-valvular atrial fibrillation. Materials and Methods: The author retrospectively studied patients with non-valvularatrial fibrillation at Pranangklao Hospital between January 2019 and October 2020. Results: One hundred thirty patients were enrolled. The average ages of the patients were 67.5±10.2 years. The average SAMe-TT₂R₂ scores were 3.2±0.8 and the average CHA₂DS₂-VASc score was 3.3±1.4. Most patients had hypertension and dyslipidemia. Most patients were prescribed betablockers. Most patients had time in therapeutic range (TTR) lower than 65. The present study has shown that patients with SAMe-TT₂R₂ score of 3 or more has also had high proportion of labile INR with statical significance. The sensitivity, specificity, positive predictive value, and negative predictive value of different cut-offs of SAMe-TT₂R₂ score greater than 2 and SAMe-TT₂R₂ score when excluding race showed improvement of the sensitivity and specificity for prediction of labile INR. Conclusion: Labile INR was predicted by SAMe-TT₂R₂ score and the sensitivity and specificity increased in SAMe-TT₂R₂ score when excluding race. Keywords: SAMe-TT₂R₂ score; Non-valvular atrial fibrillation; Anticoagulation control

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