Expertise in colonoscopy intubation does not predict diagnostic accuracy: a simulation-based study.
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ABSTRACT:
SUBMITTER: Vilmann AS
PROVIDER: S-EPMC8759934 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:AbstractPain during colonoscopy is a critical quality indicator and often a limiting factor for unsedated colonoscopy. This study aimed to identify factors associated with pain during colonoscopy and establish a model for predicting a painful colonoscopy.Patients aged 18 to 80 who underwent unsedated colonoscopy were prospectively enrolled in 2 tertiary endoscopic centers in China. The primary outcome was the rate of painful colonoscopy and then we identify high-risk factors associated with painful colonoscopy. A prediction model with an intubation discomfort score (IDS) was developed and validated.Totally 607 patients participated in this study, including 345 in the training cohort and 262 in the validation cohort. Body mass index (BMI) of <18.5 kg/m2 (OR 2.18, 95% CI: 1.09-4.37), constipation (OR 2.45, 95% CI: 1.25-4.80), and anticipating moderate or severe pain (OR 2.06, 95% CI: 1.12-3.79) were identified as independent predictive factors for painful colonoscopy and used to develop the IDS (all P < .05). Patients with IDS ≥1 had increased insertion time [9.32(6.2-13.7)] minutes vs 6.87(5.1-10.4) minutes, P = .038) and decreased cecal intubation rate (96.0% vs 99.6%, P = .044). Abdominal compression (48.4% vs 19.9%, P < .001) and position change (59.7% vs 32.1%, P < .001) were more frequently required in the group of patients with IDS ≥1. These results were externally validated in a validation cohort.The intubation discomfort score developed in this study was useful for predicting pain during colonoscopy, with IDS ≥1 indicating painful colonoscopy.
| S-EPMC7969226 | biostudies-literature
Project description:BackgroundClinical practice of Chinese medicine requires little information for differentiation of Zang-fu patterns. This study is to test the impact of information amount on the diagnostic accuracy of pattern differentiation algorithm (PDA) using stochastic simulation of cases.MethodsA dataset with 69 Zang-fu single patterns was used with manifestations according to the Four Examinations, namely inspection (Ip), auscultation and olfaction (AO), inquiry (Iq) and palpation (P). A variable quantity of available information (N(%)) was randomly sampled to generate 100 true positive and 100 true negative manifestation profiles per pattern to which PDA was applied. Four runs of simulations were used according to the Four Examinations: Ip, Ip+AO, Ip+AO+Iq and Ip+AO+Iq+P. The algorithm performed pattern differentiation by ranking a list of diagnostic hypotheses by the amount of explained information F(%). Accuracy, sensitivity, specificity and negative and positive predictive values were calculated.ResultsUse the Four Examinations resulted in the best accuracy with the smallest cutoff value (N(%)= 28.5%), followed by Ip+AO+Iq (33.5%), Ip+AO (51.5%) and Ip (52.0%). All tested combinations provided concave-shaped curves for accuracy, indicating an optimal value subject to N(%-cutoff). Use of N(%-cutoff) as a secondary criterion resulted in 94.7% (94.3; 95.1) accuracy, 89.8% (89.1; 90.6) sensitivity, and 99.5% (99.3; 99.7) specificity with the Four Examinations.ConclusionPattern differentiation based on both explained and optimum available information (F(%) and N(%-cutoff)) is more accurate than using explained and available information without cutoff (F(%) and N(%)). Both F(%) and N(%-cutoff) should be used as PDA's objective criteria to perform Zang-fu single pattern differentiation.
| S-EPMC2806360 | biostudies-literature
Project description:Symptoms have been used to diagnose conditions such as frailty and mental illnesses. However, the diagnostic accuracy of the numbers of symptoms has not been well studied. This study aims to use equations and simulations to demonstrate how the factors that determine symptom incidence influence symptoms' diagnostic accuracy for disease diagnosis. Assuming a disease causing symptoms and correlated with the other disease in 10,000 simulated subjects, 40 symptoms occurred based on 3 epidemiological measures: proportions diseased, baseline symptom incidence (among those not diseased), and risk ratios. Symptoms occurred with similar correlation coefficients. The sensitivities and specificities of single symptoms for disease diagnosis were exhibited as equations using the three epidemiological measures and approximated using linear regression in simulated populations. The areas under curves (AUCs) of the receiver operating characteristic (ROC) curves was the measure to determine the diagnostic accuracy of multiple symptoms, derived by using 2 to 40 symptoms for disease diagnosis. With respect to each AUC, the best set of sensitivity and specificity, whose difference with 1 in the absolute value was maximal, was chosen. The results showed sensitivities and specificities of single symptoms for disease diagnosis were fully explained with the three epidemiological measures in simulated subjects. The AUCs increased or decreased with more symptoms used for disease diagnosis, when the risk ratios were greater or less than 1, respectively. Based on the AUCs, with risk ratios were similar to 1, symptoms did not provide diagnostic values. When risk ratios were greater or less than 1, maximal or minimal AUCs usually could be reached with less than 30 symptoms. The maximal AUCs and their best sets of sensitivities and specificities could be well approximated with the three epidemiological and interaction terms, adjusted R-squared ≥ 0.69. However, the observed overall symptom correlations, overall symptom incidence, and numbers of symptoms explained a small fraction of the AUC variances, adjusted R-squared ≤ 0.03. In conclusion, the sensitivities and specificities of single symptoms for disease diagnosis can be explained fully by the at-risk incidence and the 1 minus baseline incidence, respectively. The epidemiological measures and baseline symptom correlations can explain large fractions of the variances of the maximal AUCs and the best sets of sensitivities and specificities. These findings are important for researchers who want to assess the diagnostic accuracy of composite diagnostic criteria.
| S-EPMC9378763 | biostudies-literature
Project description:OBJECTIVE:The English Bowel Cancer Screening Programme (BCSP) recommends 3?yearly colonoscopy surveillance for patients at intermediate risk of colorectal cancer (CRC) postpolypectomy (those with three to four small adenomas or one ?10?mm). We investigated whether faecal immunochemical tests (FITs) could reduce surveillance burden on patients and endoscopy services. DESIGN:Intermediate-risk patients (60-72 years) recommended 3?yearly surveillance were recruited within the BCSP (January 2012-December 2013). FITs were offered at 1, 2 and 3?years postpolypectomy. Invitees consenting and returning a year 1 FIT were included. Participants testing positive (haemoglobin ?40?µg/g) at years one or two were offered colonoscopy early; all others were offered colonoscopy at 3?years. Diagnostic accuracy for CRC and advanced adenomas (AAs) was estimated considering multiple tests and thresholds. We calculated incremental costs per additional AA and CRC detected by colonoscopy versus FIT surveillance. RESULTS:74% (5938/8009) of invitees were included in our study having participated at year 1. Of these, 97% returned FITs at years 2 and 3. Three-year cumulative positivity was 13% at the 40?µg/g haemoglobin threshold and 29% at 10?µg/g. 29 participants were diagnosed with CRC and 446 with AAs. Three-year programme sensitivities for CRC and AAs were, respectively, 59% and 33% at 40?µg/g, and 72% and 57% at 10?µg/g. Incremental costs per additional AA and CRC detected by colonoscopy versus FIT (40?µg/g) surveillance were £7354 and £180 778, respectively. CONCLUSIONS:Replacing 3?yearly colonoscopy surveillance in intermediate-risk patients with annual FIT could reduce colonoscopies by 71%, significantly cut costs but could miss 30%-40% of CRCs and 40%-70% of AAs. TRIAL REGISTRATION NUMBER:ISRCTN18040196; Results.
| S-EPMC6709777 | biostudies-literature
Project description:A variety of instruments are used to perform airway management by tracheal intubation. In this study, we compared the MacIntosh balde (MB) laryngoscope with the Bonfils intubation fibrescope as intubation techniques. The aim of this study was to identify the technique (MB or Bonfils) that would allow students in their last year of medical school to perform tracheal intubation faster and with a higher success probability. Data were collected from 150 participants using an airway simulator ['Laerdal Airway Management Trainer' (Laerdal Medical AS, Stavanger, Norway)]. The participants were randomly assigned to a sequence of techniques to use. Four consecutive intubation 'trials' were performed with each technique. These trials were evaluated for differences in the following categories: the 'time to successful ventilation', 'success probability' within 90 s,'time to visualisation' of the vocal cords (glottis), and 'quality of visualisation' according to the Cormack and Lehane score (C&L, grade 1-4). The primary endpoint was the 'time to successful ventilation'in the fourth and final trial.There was no statistically significant difference in the 'time to successful ventilation' between the two techniques in trial 4 ('time to successful ventilation': median: MB: 16 s, Bonfils: 14 s, p = 0.244). However, the 'success probability' within 90 s was higher when using a Macintosh blade than when using a Bonfils (95 vs. 87%). The glottis could be better visualised when using a Bonfils (C&L score of 1 (best view): MB: 41%, Bonfils: 93%), but visualisation was achieved more rapidly when using a Macintosh blade (median: 'time to visualisation': MB: 6 s, Bonfils: 8 s, p = 0.003).The time to ventilation using the MacIntosh blade and Bonfils mainly did to differ, however success probabilities and time to visualisation primary favoured the MacIntosh blade as intubation technique, although the Bonfils seem to have a steeper learning curve. The Bonfils is still a promising intubation technique and might be easier to learn as the MB, at least in a manikin.
| S-EPMC4769496 | biostudies-literature
Project description:BackgroundA large number of web-based COVID-19 symptom checkers and chatbots have been developed; however, anecdotal evidence suggests that their conclusions are highly variable. To our knowledge, no study has evaluated the accuracy of COVID-19 symptom checkers in a statistically rigorous manner.ObjectiveThe aim of this study is to evaluate and compare the diagnostic accuracies of web-based COVID-19 symptom checkers.MethodsWe identified 10 web-based COVID-19 symptom checkers, all of which were included in the study. We evaluated the COVID-19 symptom checkers by assessing 50 COVID-19 case reports alongside 410 non-COVID-19 control cases. A bootstrapping method was used to counter the unbalanced sample sizes and obtain confidence intervals (CIs). Results are reported as sensitivity, specificity, F1 score, and Matthews correlation coefficient (MCC).ResultsThe classification task between COVID-19-positive and COVID-19-negative for "high risk" cases among the 460 test cases yielded (sorted by F1 score): Symptoma (F1=0.92, MCC=0.85), Infermedica (F1=0.80, MCC=0.61), US Centers for Disease Control and Prevention (CDC) (F1=0.71, MCC=0.30), Babylon (F1=0.70, MCC=0.29), Cleveland Clinic (F1=0.40, MCC=0.07), Providence (F1=0.40, MCC=0.05), Apple (F1=0.29, MCC=-0.10), Docyet (F1=0.27, MCC=0.29), Ada (F1=0.24, MCC=0.27) and Your.MD (F1=0.24, MCC=0.27). For "high risk" and "medium risk" combined the performance was: Symptoma (F1=0.91, MCC=0.83) Infermedica (F1=0.80, MCC=0.61), Cleveland Clinic (F1=0.76, MCC=0.47), Providence (F1=0.75, MCC=0.45), Your.MD (F1=0.72, MCC=0.33), CDC (F1=0.71, MCC=0.30), Babylon (F1=0.70, MCC=0.29), Apple (F1=0.70, MCC=0.25), Ada (F1=0.42, MCC=0.03), and Docyet (F1=0.27, MCC=0.29).ConclusionsWe found that the number of correctly assessed COVID-19 and control cases varies considerably between symptom checkers, with different symptom checkers showing different strengths with respect to sensitivity and specificity. A good balance between sensitivity and specificity was only achieved by two symptom checkers.
| S-EPMC7541039 | biostudies-literature
Project description:The complex structure of the brain supports high-order cognition, which is crucial for mastering chess. Surface-based measures, including the fractional dimension (FD) and gyrification index (GI), may be more sensitive in detecting cortical changes relative to volumetric indexes. For this reason, structural magnetic resonance imaging data from 29 chess experts and 29 novice participants were analyzed using the CAT12 toolbox. FD and GI for each brain region were compared between the groups. A multivariate model was used to identify surface-based brain measures that can predict chess expertise. In chess experts, FD is increased in the left frontal operculum (p < 0.01), and this change correlates with the starting age of chess practice (ρ = -0.54, p < 0.01). FD is decreased in the right superior parietal lobule (p < 0.01). Chess expertise is predicted by the FD in a network of fronto-parieto-temporal regions and is associated with GI changes in the middle cingulate gyrus (p < 0.01) and the superior temporal sulcus (p < 0.01). Our findings add to the evidence that chess expertise is based on the complex properties of the brain surface of a network of transmodal association areas important for flexible high-level cognitive functions. Interestingly, these changes are associated with long-lasting practice, suggesting that neuroplastic effects develop over time.
| S-EPMC9688322 | biostudies-literature
Project description:INTRODUCTION. Liquid biopsies are a minimally invasive collection of a patient body fluid sample. In oncology, they offer several advantages compared to traditional tissue biopsies. However, potential of this method in endometrial cancer (EC) remains poorly explored. We studied the utility of tumor educated platelets (TEPs) and circulating tumor DNA (ctDNA) for preoperative EC diagnosis, including histology determination. MATERIALS AND METHODS. TEPs from 297 subjects (53 EC patients, 40 patients with benign gynecologic conditions and 204 healthy women) were RNA-sequenced. DNA sequencing was performed in 519 primary tumor tissue samples and in16 plasma samples. Artificial intelligence was applied to sample classification. RESULTS. Platelet-dedicated classifier yielded AUC of 93.1% in test set when discriminating between healthy subjects and cancer patients. However, the discrimination between endometrial cancer and benign gynecologic conditions was relatively low, with AUC of 60.7%. ctDNA-dedicated classifier discriminated primary tumor tissue samples with AUC of 91.4% and ctDNA blood samples with AUC of 87.5%. CONCLUSIONS Liquid biopsies show potential in EC diagnosis. Both TEPs and ctDNA profiles coupled with artificial intelligence constitute a source of useful information. Further work, involving more cases, is warranted.
2021-12-01 | GSE184904 | GEO
Project description:In acute aortic syndromes (AAS), organ malperfusion represents a key event impacting both on diagnosis and outcome. Increased levels of plasma lactate dehydrogenase (LDH), a biomarker of malperfusion, have been reported in AAS, but the performance of LDH for the diagnosis of AAS and the relation of LDH with outcome in AAS have not been evaluated so far.This was a bi-centric prospective diagnostic accuracy study and a cohort outcome study. From 2008 to 2014, patients from 2 Emergency Departments suspected of having AAS underwent LDH assay at presentation. A final diagnosis was obtained by aortic imaging. Patients diagnosed with AAS were followed-up for in-hospital mortality.One thousand five hundred seventy-eight consecutive patients were clinically eligible, and 999 patients were included in the study. The final diagnosis was AAS in 201 (20.1%) patients. Median LDH was 424 U/L (interquartile range [IQR] 367-557) in patients with AAS and 383 U/L (IQR 331-460) in patients with alternative diagnoses (P < 0.001). Using a cutoff of 450 U/L, the sensitivity of LDH for AAS was 44% (95% confidence interval [CI] 37-51) and the specificity was 73% (95% CI 69-76). Overall in-hospital mortality for AAS was 23.8%. Mortality was 32.6% in patients with LDH ≥ 450 U/L and 16.8% in patients with LDH < 450 U/L (P = 0.006). Following stratification according to LDH quartiles, in-hospital mortality was 12% in the first (lowest) quartile, 18.4% in the second quartile, 23.5% in the third quartile, and 38% in the fourth (highest) quartile (P = 0.01). LDH ≥ 450 U/L was further identified as an independent predictor of death in AAS both in univariate and in stepwise logistic regression analyses (odds ratio 2.28, 95% CI 1.11-4.66; P = 0.025), in addition to well-established risk markers such as advanced age and hypotension. Subgroup analysis showed excess mortality in association with LDH ≥ 450 U/L in elderly, hemodynamically stable and in nonsurgically treated patients.Plasma LDH constitutes a biomarker of poor outcome in patients with AAS. LDH is a rapid and universally available assay that could be used to improve risk stratification and to individualize treatment in patient groups where options are controversial.
| S-EPMC4753927 | biostudies-literature
Project description:To assess their utility in routine neuropathology, we prospectively integrated DNA methylation-based CNS tumor classification and targeted gene panel sequencing of tumor and constitutional DNA with blinded neuropathological reference diagnostics for a population-based cohort of > 1,200 newly-diagnosed pediatric patients.
2023-01-30 | GSE215240 | GEO