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As cost-effective measures become increasingly implemented in the US healthcare system, changes in patient-reported outcome measure (PROM) scores can be utilized to indicate patient satisfaction following procedures including total knee arthroplasty (TKA). The primary aim of this study was to develop and evaluate machine learning algorithms to predict achievement of the minimal clinically important difference (MCID) for the Knee Injury and Osteoarthritis Outcome Score-Physical Function Short Form (KOOS-PS) at 1-year following TKA. A retrospective review of primary TKA patients between 2016 and 2018 was performed. Variables considered for prediction included demographics and preoperative PROMs. The KOOS-PS MCID was calculated via a distribution-based method. Five machine learning algorithms were developed and tested by discrimination, calibration, Brier score, and decision curve analysis. Among the 744 patients who met the inclusion criteria, 385 (72.8%) patients achieved the MCID. The elastic-net penalized logistic regression model was selected as the best performing model (c-statistic 0.77, calibration intercept -0.02, calibration slope 1.15, and Brier score 0.14). The most important variables for MCID achievement were preoperative KOOS-PS score, preoperative VAS Pain, preoperative opioid use, preoperative PROMIS global mental health score, age, and sex. Algorithms were incorporated into an open-access digital application available at https://sorg-apps.shinyapps.io/tka_koos_mcid/. This study is the first to predict the probability of achieving the KOOS-PS MCID following TKA using a machine learning-based approach. The results were used to develop a clinical decision aid based on commonly collected predictive variables to preoperatively predict an individual patient's likelihood of attaining an acceptable outcome following TKA.

Background Current the surveillance and management are controversial for patients with IPMN. We aimed to develop an alternative nomogram to individualize IPMN prognosis and LNM. Methods Based on the data from SEER database of patients diagnosed with IPMN between 2004 and 2015, a nomogram predicting the survival and LNM of IPMN based on univariate and multivariate and Lasso regression analysis was performed, internally and externally validated, and measured by C‐index, and decision curve analysis (DCA), and compared to the 7th TNM stage. Results A total of 941 patients were included. Age, T stage examined nodes, tumor size, and pathology grade were identified as an independent factor for predicting LNM. The nomogram we established to predict LNM had a high predicting value with a C‐index value of 0.735 and an AUC value of 0.753. Interestingly, including T1 stage, we found an inverse correlation was between age and LNM. In addition, nomogram for predicting CSS also performed better than TNM stage both in the internal validation group (1‐year AUC:0.753 vs. 0.693, 3‐year AUC: 0.801 vs. 0.731, 5‐year AUC: 0.803 vs. 0.733) and external validation group (1‐year AUC: 0.761 vs. 0.701, 3‐year AUC: 0.772 vs. 0.713, 5‐year AUC:0.811 vs. 0.735). DCA analysis showed the nomogram showed a greater benefit across the period of follow‐up compared to 7th TNM stage. Conclusion A nomogram based on multivariate and Lasso regression analysis showed great clinical usability compared with current criteria. Also, for LNM of IPMN, younger age patients with IPMN should be attached more importance., First, we build a nomogram to predict lymph node metastasis of patients with IPMN, which had a high predicting value with an C‐index value of 0.735 and an AUC of 0.753. Second, mutivariate logistic regression analysis showed age was an independent factor and heat map showed patients aged 20–39 had highest lymph node metastasis rate while patients aged >=80 had least rate. Moreover, subgroup analysis showed patients aged 20–39 years had highest LNM rate while patients aged >=80 had the lowest LNM rate in most subgroups except some values were doubted. Third, we performed Lasso regression analysis and identified the features that are highly associated with survival, at the same time, nomogram was performed based on multivariate cox regression analysis, internally and externally validated, and measured by ROC curve, C‐index, and decision curve analysis (DCA), and compared to the 7th TNM stage. DCA analysis showed no matter in the internal cohort or external cohort, the nomogram showed a greater benefit across the period of follow‐up compared to 7th TNM stage.

BACKGROUND The aims of this study were to develop a prediction model for identification of individuals with diabetes based on clinical and perceived periodontal measurements; and to evaluate its added value when combined with standard diabetes screening tools. METHODS The study was carried out among 250 adults attending primary care clinics in Riyadh (Saudi Arabia). The study adopted a case-control approach, where diabetes status was first ascertained, and the Finnish Diabetes Risk Score (FINDRISC), Canadian Diabetes Risk questionnaire (CANRISK), and periodontal examinations were carried out afterward. RESULTS A periodontal prediction model (PPM) including three periodontal indicators (missing teeth, percentage of sites with pocket probing depth ≥6 mm, and mean pocket probing depth) had an area under the curve (AUC) of 0.694 (95% Confidence Interval: 0.612-0.776) and classified correctly 62.4% of participants. The FINDRISC and CANRISK tools had AUCs of 0.766 (95% CI: 0.690-0.843) and 0.821 (95% CI: 0.763-0.879), respectively. The addition of the PPM significantly improved the AUC of FINDRISC (P = 0.048) but not of CANRISK (P = 0.144), with 26.8% and 9.8% of participants correctly reclassified, respectively. Finally, decision curve analysis showed that adding the PPM to both tools would result in net benefits among patients with probability scores lower than 70%. CONCLUSIONS This study showed that periodontal measurements could play a role in identifying individuals with diabetes, and that addition of clinical periodontal measurements improved the performance of FINDRISC and CANRISK.

Objective To evaluate clinical prediction tools for making decisions in patients with severe urinary tract infections (UTIs). Methods This was a retrospective study conducted at 2 hospitals (combined emergency department (ED) census 190,000). Study patients were admitted via the ED with acute pyelonephritis or severe sepsis‐septic shock related UTI. Area under the receiver operating characteristic curve (AUROC) augmented by decision curve analysis and sensitivity of each rule for predicting mortality and ICU admission were compared. Results The AUROC of PRACTICE was greater than that of BOMBARD (0.15 difference, 95% confidence interval [CI] = 0.09–0.22), SIRS (0.21 difference, 95% CI = 0.14–0.28) and qSOFA (0.06 difference, 95% CI = 0–0.11) for predicting mortality. PRACTICE had a greater net benefit compared to BOMBARD and SIRS at all thresholds and a greater net benefit compared to qSOFA between a 1% and 10% threshold probability level for predicting mortality. PRACTICE had a greater net benefit compared to all other scores for predicting ICU admission across all threshold probabilities. A PRACTICE score >75 was more sensitive than a qSOFA score >1 (90% versus 54.3%, 35.7 difference, 95% CI = 24.5–46.9), SIRS criteria >1 (18.6 difference, 95% CI = 9.5–27.7), and a BOMBARD score >2 (12.9 difference, 95% CI = 5–12.9) for predicting mortality. Conclusion PRACTICE was more accurate than BOMBARD, SIRS, and qSOFA for predicting mortality. PRACTICE had a superior net benefit at most thresholds compared to other scores for predicting mortality and ICU admissions.

At the time of diagnosis, approximately 15%‐20% of patients with rectal cancer (RC) presented synchronous liver metastasis (SLM), which is the most common cause of death in patients with RC. Therefore, preoperative, noninvasive, and accurate prediction of SLM is crucial for personalized treatment strategies. Recently, radiomics has been considered as an advanced image analysis method to evaluate the neoplastic heterogeneity with respect to diagnosis of the tumor and prediction of prognosis. In this study, a total of 1409 radiomics features were extracted for each volume of interest (VOI) from high‐resolution T2WI images of the primary RC. Subsequently, five optimal radiomics features were selected based on the training set using the least absolute shrinkage and selection operator (LASSO) method to construct the radiomics signature. In addition, radiomics signature combined with carcinoembryonic antigen (CEA) and carbohydrate antigen 19‐9 (CA19‐9) was included in the multifactor logistic regression to construct the nomogram model. It showed an optimal predictive performance in the validation set as compared to that in the radiomics model. The favorable calibration of the radiomics nomogram showed a nonsignificant Hosmer‐Lemeshow test statistic (P > .05). The decision curve analysis (DCA) showed that the radiomics nomogram is clinically superior to the radiomics model. Therefore, the nomogram amalgamating the radiomics signature and clinical risk factors serve as an effective quantitative approach to predict the SLM of primary RC., In the present study, we aimed to evaluate the predictive performance of radiomics nomogram for the diagnosis of synchronous liver metastasis in rectal cancer patients.

Background/Aims Pancreatic ductal adenocarcinoma (PDAC) is associated with high mortality, even after surgical resection. The existing predictive models for survival have limitations. This study aimed to develop better nomograms for predicting overall survival (OS) and cancer‐specific survival (CSS) in PDAC patients after surgery. Methods A total of 6323 PDAC patients were retrospectively recruited from the Surveillance, Epidemiology, and End Results (SEER) database and randomly allocated into training, validation, and test cohorts. Multivariate Cox regression analysis was conducted to identify significant independent factors for OS and CSS, which were used for construction of nomograms. The performance was evaluated, validated, and compared with that of the 8th edition AJCC staging system. Results Ten independent factors were significantly correlated with OS and CSS. The 1‐, 3‐, and 5‐year OS rates were 40%, 20%, and 15%, and 1‐, 3‐, and 5‐year CSS rates were 45%, 24%, and 19%, respectively. The nomograms were calibrated well, with c‐indexes of 0.640 for OS and 0.643 for CSS, respectively. Notably, relative to the 8th edition AJCC staging system, the nomograms were able to stratify each AJCC stage into three prognostic subgroups for more robust risk stratification. Furthermore, the nomograms achieved significant clinical validity, exhibiting wide threshold probabilities and high net benefit. Performance assessment also showed high predictive accuracy and reliability. Conclusions The predictive ability and reliability of the established nomograms have been validated, and therefore, these nomograms hold potential as novel approaches to predicting survival and assessing survival risks for PDAC patients after surgery., In the present study, we constructed and validated nomograms for predicting the 1‐, 3‐, and 5‐year OS and CSS rates after surgical resection of PDAC. The nomograms established in the present study showed good performance for the prediction of OS and CSS, which was superior to that of the existing, conventional AJCC staging system with an improved net benefit. Therefore, these nomograms hold promise as novel prognosis models for improving the prediction of individualized postoperative survival and improving the care of PDAC patients following surgical resection.

Background Endoscopic ultrasound-guided fine-needle aspiration is associated with the accurate determination of tumor grade. However, because it is an invasive procedure there is a need to explore alternative noninvasive procedures. Purpose To develop and validate a noncontrast radiomics model for the preoperative prediction of nonfunctional pancreatic neuroendocrine tumor (NF-pNET) grade (G). Study type Retrospective, single-center study. Subjects Patients with pathologically confirmed PNETs (139) were included. Field strength/sequence 3T/breath-hold single-shot fast-spin echo T2 -weighted sequence and unenhanced and dynamic contrast-enhanced T1 -weighted fat-suppressed sequences. Assessment Tumor features on contrast MR images were evaluated by three board-certified abdominal radiologists. Statistical tests Multivariable logistic regression analysis was used to develop the clinical model. The least absolute shrinkage and selection operator method and linear discriminative analysis (LDA) were used to select the features and to construct a radiomics model. The performance of the models was assessed using the training cohort (97 patients) and the validation cohort (42 patients), and decision curve analysis (DCA) was applied for clinical use. Results The clinical model included 14 imaging features, and the corresponding area under the curve (AUC) was 0.769 (95% confidence interval [CI], 0.675-0.863) in the training cohort and 0.729 (95% CI, 0.568-0.890) in the validation cohort. The LDA included 14 selected radiomics features that showed good discrimination-in the training cohort (AUC, 0.851; 95% CI, 0.758-0.916) and the validation cohort (AUC, 0.736; 95% CI, 0.518-0.874). In the decision curves, if the threshold probability was 0.17-0.84, using the radiomics score to distinguish NF-pNET G1 and G2/3, offered more benefit than did the use of a treat-all-patients or treat-none scheme. Data conclusion The developed radiomics model using noncontrast MRI could help differentiate G1 and G2/3 tumors, to make the clinical decision, and screen pNETs grade. Level of evidence 4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1124-1136.

Background Asthma remains a serious health problem with increasing prevalence and incidence. This study was to develop and validate a dynamic nomogram for predicting asthma risk. Methods Totally 597 subjects whose age ≥18 years old with asthma, an accurate age at first cigarette, and clear smoking status were selected from the National Health and Nutrition Examination Survey (NHANES) database (2013–2018). The dataset was randomly split into the training set and the testing set at a ratio of 4:6. Simple and multiple logistic regressions were used for identifying independent predictors. Then the nomogram was developed and internally validated using data from the testing set. The receiver operator characteristic (ROC) curve was used for assessing the performance of the nomogram. Results According to the simple and multiple logistic regressions, smoking ≥40 years, female gender, the age for the first smoking, having close relative with asthma were independently associated with the risk of an asthma attack. The nomogram was thereby developed with the link of https://yanglifen.shinyapps.io/Dynamic_Nomogram_for_Asthma/. The ROC analyses showed an AUC of 0.726 (0.724–0.728) with a sensitivity of 0.887 (0.847–0.928) in the training set, and an AUC of 0.702 (0.700–0.703) with a sensitivity of 0.860 (0.804–0.916) in the testing set, fitting well in calibration curves. Decision curve analysis further confirmed the clinical usefulness of the nomogram. Conclusion Our dynamic nomogram could help clinicians to assess the individual probability of asthma attack, which was helpful for improving the treatment and prognosis of asthma., We developed and internally validated a dynamic nomogram to help clinicians to estimate the individual probability of asthma, which was useful for improving the treatment and prognosis of asthma.

Background and objective Improved detectability of chronic obstructive pulmonary disease (COPD) using a handheld flow meter (HFM) with symptom-based questionnaires has not been sufficiently evaluated. This study aimed to identify the benefit of using an HFM in COPD screening. Methods A total of 2008 participants, who were ≥ 40 years of age, from Isumi City, Japan, were recruited. We developed two novel point systems for detecting COPD, one incorporated score of HFM alone (sHFM) and the other incorporated the score of International Primary Care Airway Group questionnaire (IPAG) and HFM (sIPAG + HFM). Validation using random sample allocation (split-sample validation) was carried out to assess the predictive performance of these models. Results Participants were assigned to a data set for model creation (n = 1007) or a data set for model assessment (n = 1001) to perform split-sample validation. Decision curve analysis showed that the net benefits of sHFM and sIPAG + HFM were higher than that of the IPAG score (sIPAG) and specificity of the former two were also significantly higher than that of sIPAG. However, the curves of sHFM and sIPAG + HFM were crossing and practically the same with no significant difference in sensitivity and specificity. Conclusion This study confirms that HFM is significantly advantageous in detecting COPD despite the use of a conventional questionnaire.

BACKGROUND AND OBJECTIVES Bladder cancer is one of the most common and highly recurrent cancers worldwide. Recurrence-associated genes may potentially predict cancer recurrence. We aimed to construct a recurrence-associated gene panel to improve the prognostic prediction of bladder cancer. METHODS Based on DNA sequencing and clinical data from the TCGA-BLCA project, we identified 10 potential driver genes significantly associated with recurrence of bladder cancer. We performed multivariable logistic regression analysis to construct an optimized recurrence prediction model with nine recurrence-associated genes (EME1, AKAP9, ZNF91, PARD3, STAG2, ZFP36L2, METTL3, POLR3B, and MUC7) and clinical information as the independent variables. RESULTS The area under the receiver operating characteristic (ROC) curve was 0.80 in this model, much higher than that of the baseline model (AUC = 0.73) and the same trend was also validated in its subset. Decision curve analysis also revealed that there is a significant net benefit gained by adding nine genes mutation to the baseline model. Furthermore, Kaplan-Meier survival analysis showed that eight out of the nine genes (excluding MUC7) had good effects on the overall prognosis of patients. CONCLUSIONS This nine-gene panel will most likely be a useful tool for prognostic evaluation and will facilitate the personalized management of patients with bladder cancer.

Background The value of dynamic contrast-enhanced (DCE) sequences in prostate MRI compared with noncontrast MRI is controversial. Purpose To evaluate the population net benefit of risk stratification using DCE-MRI for detection of high-grade prostate cancer (HGPCA), with or without high spatiotemporal resolution DCE imaging. Study type Decision curve analysis. Population Previously published patient studies on MRI for HGPCA detection, one using DCE with golden-angle radial sparse parallel (GRASP) images and the other using standard DCE-MRI. Field strength/sequence GRASP or standard DCE-MRI at 3 T. Assessment Each study reported the proportion of lesions with HGPCA in each Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) category (1-5), before and after reclassification of peripheral zone lesions from PI-RADS 3-4 based on contrast-enhanced images. This additional risk stratifying information was translated to population net benefit, when biopsy was hypothetically performed for: all lesions, no lesions, PI-RADS ≥3 (using NC-MRI), and PI-RADS ≥4 on DCE. Statistical tests Decision curve analysis was performed for both GRASP and standard DCE-MRI data, translating the avoidance of unnecessary biopsies and detection of HGPCA to population net benefit. We standardized net benefit values for HGPCA prevalence and graphically summarized the comparative net benefit of biopsy strategies. Results For a clinically relevant range of risk thresholds for HGPCA (>11%), GRASP DCE-MRI with biopsy of PI-RADS ≥4 lesions provided the highest net benefit, while biopsy of PI-RADS ≥3 lesions provided highest net benefit at low personal risk thresholds (2-11%). In the same range of risk thresholds using standard DCE-MRI, the optimal strategy was biopsy for all lesions (0-15% risk threshold) or PI-RADS ≥3 on NC-MRI (16-33% risk threshold). Data conclusion GRASP DCE-MRI may potentially enable biopsy of PI-RADS ≥4 lesions, providing relatively preserved detection of HGPCA and avoidance of unnecessary biopsies compared with biopsy of all PI-RADS ≥3 lesions. J. Magn. Reson. Imaging 2019;49:1400-1408.

Renal cancer is a common urogenital system malignance. Novel biomarkers could provide more and more critical information on tumor features and patients' prognosis. Here, we performed an integrated analysis on the discovery set and established a three-gene signature to predict the prognosis for clear cell renal cell carcinoma (ccRCC). By constructing a LASSO Cox regression model, a 3-messenger RNA (3-mRNA) signature was identified. Based on the 3-mRNA signature, we divided patients into high- and low-risk groups, and validated this by using three other data sets. In the discovery set, this signature could successfully distinguish between the high- and low-risk patients (hazard ratio (HR), 2.152; 95% confidence interval (CI),1.509-3.069; p < 0.0001). Analysis of internal and two external validation sets yielded consistent results (internal: HR, 2.824; 95% CI, 1.601-4.98; p < 0.001; GSE29609: HR, 3.002; 95% CI, 1.113-8.094; p = 0.031; E-MTAB-3267: HR, 2.357; 95% CI, 1.243-4.468; p = 0.006). Time-dependent receiver operating characteristic (ROC) analysis indicated that the area under the ROC curve at 5 years was 0.66 both in the discovery and internal validation set, while the two external validation sets also suggested good performance of the 3-mRNA signature. Besides that, a nomogram was built and the calibration plots and decision curve analysis indicated the good performance and clinical utility of the nomogram. In conclusion, this 3-mRNA classifier proved to be a useful tool for prognostic evaluation and could facilitate personalized management of ccRCC patients.

BACKGROUND Transcutaneous laryngeal ultrasound (TCLUS) can assess Vocal folds (VF) by subjectively identifying mobility or objectively by calculating vocal fold displacement velocity (VFDV). Optimal diagnostic approach (subjective assessment, VFDV estimation or a combination of both) is unresolved; hence, we conducted this prospective study in patients undergoing thyroidectomy. METHOD Two anaesthetists performed TCLUS pre- and post-operatively for functional assessment of 200 VFs on 100 patients. Their findings were compared with pre-operative flexible laryngoscope (FL) performed by surgeons and with post-operative C-Mac video laryngoscope (C-Mac VL) by another independent anaesthetist. Correlation between FL and TCLUS findings and inter-rater agreement between TCLUS findings of both anaesthetists was analysed. Decision curve analysis (DCA) was performed to compare clinical benefit of hoarseness, subjective VF movement, VFDV, and combined assessment for detecting disabled VFs. RESULTS We found good correlation between VF mobility on TCLUS and FL (Spearman's r = 0.93, P < 0.0001) as well as C-Mac VL (Spearman's r = 0.83, P < 0.0001) with excellent inter-rater agreement between both anaesthetists. DCA showed combined assessment to have marginally higher clinical benefit than other diagnostic approaches at intermediate threshold probabilities while its benefit was similar to subjective evaluation at higher threshold probabilities. CONCLUSION Provided achievement of optimal acoustic window, TCLUS can reliably assess disabled VFs with FL reserved for their confirmation or doubtful cases. Subjective assessment of VF mobility should suffice in most cases with additional VFDV estimation reserved pre-operatively for situations with higher risk of VFs disability, and post-operatively when subjective VF assessment findings are discordant from pre-operative status.

IntroductionPrediction models may contribute to personalized risk-based management of women at high risk of spontaneous preterm delivery. Although prediction models are published frequently, often with promising results, external validation generally is lacking. We performed a systematic review of prediction models for the risk of spontaneous preterm birth based on routine clinical parameters. Additionally, we externally validated and evaluated the clinical potential of the models.Material and methodsPrediction models based on routinely collected maternal parameters obtainable during first 16weeks of gestation were eligible for selection. Risk of bias was assessed according to the CHARMS guidelines. We validated the selected models in a Dutch multicenter prospective cohort study comprising 2614 unselected pregnant women. Information on predictors was obtained by a web-based questionnaire. Predictive performance of the models was quantified by the area under the receiver operating characteristic curve (AUC) and calibration plots for the outcomes spontaneous preterm birthResultsFour studies describing five prediction models fulfilled the eligibility criteria. Risk of bias assessment revealed a moderate to high risk of bias in three studies. The AUC of the models ranged from 0.54 to 0.67 and from 0.56 to 0.70 for the outcomes spontaneous preterm birthConclusionsThis review revealed several reporting and methodological shortcomings of published prediction models for spontaneous preterm birth. Our external validation study indicated that none of the models had the ability to predict spontaneous preterm birth adequately in our population. Further improvement of prediction models, using recent knowledge about both model development and potential risk factors, is necessary to provide an added value in personalized risk assessment of spontaneous preterm birth.

WW domain‐containing oxidoreductase (WWOX), which has a protein‐interaction domain and is regarded to be a tumor suppressor, has been known to play an important role in anti‐angiogenesis and cancer progression. This study aimed to investigate prognostic values of WWOX expression in hepatocellular carcinoma (HCC) patients after hepatectomy. Additionally, we intended to formulate a valuable prognostic nomogram for HCCs. 182 HCC patients who underwent hepatectomy from January 2009 to January 2010 were enrolled in our study. qRT‐PCR, Western blot, and immunohistochemistry on tissue microarrays were then used to determine the expression levels of WWOX. An evaluation of the role of WWOX expression levels in the prognosis and outcome of patients was established. A decrease in the expression of WWOX was found when compared to adjacent tumor‐free tissues, which led to worse overall survival (OS) and recurrence‐free survival (RFS) and, therefore, was considered as an independent negative factor in the prognosis of HCC. Two nomograms, comprising WWOX, alpha‐fetoprotein (AFP), tumor size, and γ‐glutamyltransferase (γ‐GT), were constructed to obtain superior discriminatory abilities than conventional staging systems in terms of C‐index and clinical net benefit on decision curve analysis (DCA) for OS and RFS. Our data suggest that WWOX expression is strongly related to HCC post‐resection aggressiveness and recurrence. Additional advanced and accurate predictive model through the incorporation of WWOX into nomogram could help predict OS or RFS for HCC patients.

Introduction Prediction models may contribute to personalized risk‐based management of women at high risk of spontaneous preterm delivery. Although prediction models are published frequently, often with promising results, external validation generally is lacking. We performed a systematic review of prediction models for the risk of spontaneous preterm birth based on routine clinical parameters. Additionally, we externally validated and evaluated the clinical potential of the models. Material and methods Prediction models based on routinely collected maternal parameters obtainable during first 16 weeks of gestation were eligible for selection. Risk of bias was assessed according to the CHARMS guidelines. We validated the selected models in a Dutch multicenter prospective cohort study comprising 2614 unselected pregnant women. Information on predictors was obtained by a web‐based questionnaire. Predictive performance of the models was quantified by the area under the receiver operating characteristic curve (AUC) and calibration plots for the outcomes spontaneous preterm birth

BACKGROUND AND PURPOSE We aimed to test the clinical utility of the leg:thigh intraepidermal nerve-fiber (IENF) density ratio as a parameter to discriminate between length-dependent small-fiber neuropathy (SFN) and small-fiber sensory ganglionopathy (SFSG) in subjects with signs and symptoms of small-fiber pathology. METHODS We retrospectively evaluated thigh and leg IENF density in 314 subjects with small-fiber pathology (173 with distal symmetrical length-dependent SFN and 141 with non-length-dependent SFSG). A group of 288 healthy subjects was included as a control group. The leg:thigh IENF density ratio was calculated for all subjects. We used receiver operating characteristic curve analyses to assess the ability of this parameter to discriminate between length-dependent SFN and SFSG, and the decision curve analysis to estimate its net clinical benefit. RESULTS In patients with neuropathy, the mean IENF density was 14.8 ± 6.8/mm at the thigh (14.0 ± 6.9/mm in length-dependent SFN and 15.9 ± 6.7/mm in patients with SFSG) and 7.5 ± 4.5/mm at the distal leg (5.4 ± 3.2/mm in patients with length-dependent SFN and 10.1 ± 4.6/mm in patients with SFSG). The leg:thigh IENF density ratio was significantly (P < 0.01) lower in patients with length-dependent SFN (0.44 ± 0.23) compared with patients with SFSG (0.68 ± 0.28). The area under the curve of the receiver operating characteristic analysis to discriminate between patients with length-dependent SFN and SFSG was 0.79. The decision curve analysis demonstrated the clinical utility of this parameter. CONCLUSIONS The leg:thigh IENF ratio represents a valuable tool in the differential diagnosis between SFSG and length-dependent SFN.

The use of data from multiple studies or centers for the validation of a clinical test or a multivariable prediction model allows researchers to investigate the test's/model's performance in multiple settings and populations. Recently, meta-analytic techniques have been proposed to summarize discrimination and calibration across study populations. Here, we rather consider performance in terms of net benefit, which is a measure of clinical utility that weighs the benefits of true positive classifications against the harms of false positives. We posit that it is important to examine clinical utility across multiple settings of interest. This requires a suitable meta-analysis method, and we propose a Bayesian trivariate random-effects meta-analysis of sensitivity, specificity, and prevalence. Across a range of chosen harm-to-benefit ratios, this provides a summary measure of net benefit, a prediction interval, and an estimate of the probability that the test/model is clinically useful in a new setting. In addition, the prediction interval and probability of usefulness can be calculated conditional on the known prevalence in a new setting. The proposed methods are illustrated by 2 case studies: one on the meta-analysis of published studies on ear thermometry to diagnose fever in children and one on the validation of a multivariable clinical risk prediction model for the diagnosis of ovarian cancer in a multicenter dataset. Crucially, in both case studies the clinical utility of the test/model was heterogeneous across settings, limiting its usefulness in practice. This emphasizes that heterogeneity in clinical utility should be assessed before a test/model is routinely implemented. ispartof: Statistics In Medicine vol:37 issue:12 pages:2034-2052 ispartof: location:England status: published

Objectives To assess the level of agreement between digital rectal examination findings of two urologists and its effect on risk prediction using the digital rectal examination-based Rotterdam Prostate Cancer Risk Calculator. Methods The study sample consisted of a prospective cohort of asymptomatic unscreened men with prostate-specific antigen ≤50.0 ng/mL and transrectal ultrasound volume ≤110 mL who underwent transrectal ultrasound-guided prostate biopsy. Both urologists’ digital rectal examination findings were graded normal or abnormal (nodularity and/or induration), and volume classified as 25, 40 or 60 mL, according to the risk calculator algorithm. Interrater agreement analysis using Cohen's kappa (κ) statistic was carried out to determine consistency of digital rectal examination outcome and volume assessment. Receiver operating characteristic curve analysis and calibration plots were constructed to determine the effect of interrater differences. Decision curve analysis was applied to evaluate the clinical usefulness of the model. Results Of the 241 men included in the study, 41% (n = 98) had prostate cancer (81 were clinically significant, i.e. Gleason ≥3 + 4). There was substantial agreement in the digital rectal examination (abnormal/normal; κ = 0.78; P

Objective To assess the external validity of all published first‐trimester prediction models based on routinely collected maternal predictors for the risk of small‐ and large‐for‐gestational‐age (SGA and LGA) infants. Furthermore, the clinical potential of the best‐performing models was evaluated. Design Multicentre prospective cohort. Setting Thirty‐six midwifery practices and six hospitals (in the Netherlands). Population Pregnant women were recruited at 90th percentile occurred in 203 and 224 women, respectively. The C‐statistics of the included models ranged from 0.52 to 0.64 for SGA (n = 6), and from 0.60 to 0.69 for LGA (n = 6). All models yielded higher C‐statistics for more severe cases of SGA (95th percentile). Initial calibration showed poor‐to‐moderate agreement between the predicted probabilities and the observed outcomes, but this improved substantially after recalibration. Conclusion The clinical relevance of the models is limited because of their moderate predictive performance, and because the definitions of SGA and LGA do not exclude constitutionally small or large infants. As most clinically relevant fetal growth deviations are related to ‘vascular’ or ‘metabolic’ factors, models predicting hypertensive disorders and gestational diabetes are likely to be more specific. Tweetable abstract The clinical relevance of prediction models for the risk of small‐ and large‐for‐gestational‐age is limited., Tweetable abstract The clinical relevance of prediction models for the risk of small‐ and large‐for‐gestational‐age is limited.