Sensitivity and specificity of mid-upper arm circumference for assessment of severe acute malnutrition among children ages 6 to 59 months: Systematic review and meta-analysis.

OBJECTIVES: Sensitivity is the proportion of people classified as diseased (i.e., no false negatives). A test with low sensitivity can be thought of as being too cautious in finding a positive result. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines were followed for this systematic review and meta-analysis. The databases used were PubMed, Google Scholar, Jane, and African Journals Online. The search terms used were "sensitivity" and "specificity of and mid-upper arm circumference" (MUAC). A Joanna Briggs Institute meta-analysis and checklist for diagnostic test accuracy studies was used for the critical appraisal of the studies. The meta-analysis was conducted using STATA, version 14, software. The pooled sensitivity was computed to present the pooled sensitivity at a 95% confidence interval (CI). RESULTS: A total of 11 individual studies were included in the meta-analysis. The lowest sensitivity of MUAC with the detection of severe acute malnutrition (SAM) was 5% in Vietnam, and the highest sensitivity was at 43.2% in India. The pooled sensitivity of MUAC among children aged <5 y to determine SAM was 20.7% (range, 13.24%-28.25%; P = 0.001). Based on the pooled specificity of MUAC, the detection of SAM was 97.636% (95% CI, 96.339%-98.932%; P = 0.001), and the pooled optimal cutoff point to diagnose SAM was 13.23 cm (95% CI, 12.692-13.763 cm; P = 0.001). CONCLUSIONS: The sensitivity of MUAC is lower compared with the specificity to detect SAM, and varies from area to area.

[MiR-4772 modulates tumor immune microenvironment by regulating immune- related genes in ovarian cancer].

OBJECTIVE: To explore the regulatory role of miR-4772 in the formation of tumor immune microenvironment in ovarian cancer. METHODS: The optimal cutoff level of PD-L1 expression was calculated based on data from 294 ovarian cancer patients in the TCGA database. The differentially expressed genes (DEGs) between high and low PD-L1 expression groups were screened, and the important DEGs were identified by correlation analysis. WGCNA analysis was performed to select the weighted genes and PD-L1-related miRNAs, from which the hub genes were obtained by intersection analysis. ssGSEA analysis was used to evaluate the effect of PD-L1 and miR-4772 expressions on the tumor immune microenvironment in ovarian cancer. KEGG analysis was used to identify the involved signal pathways, and the interactions between the hub genes were mapped by protein-protein interaction (PPI) analysis. Survival analysis was carried out to identify the survival-related hub genes, and the results were validated using the data of 399 patients with ovarian cancer from GEO database and the sequencing results of SKOV3 cells transfected with miR-4772 mimics or inhibitor. RESULTS: According the optimal cutoff level of PD-L1 expression of 1.31582 (90th quantile), the patients were divided into high- and low-PD-L1 expression groups. A total of 840 DEGs were identified, including 549 significantly up-regulated genes and 291 down-regulated genes. Among them, 20 important DEGs were found to closely correlate with miR-4772 expression, and WGCNA analysis identified 48 weighted genes significantly correlated with miR-4772. Twelve genes were identified as both key DEGs and weighted genes and were treated as the hub genes. ssGSEA analysis showed that both the patients with high PD-L1 expressions and those with high miR-4772 expressions showed more active immune infiltration and functional activity. The 12 hub genes were involved mainly in immune-related signaling pathways, and PPI analysis suggested significant interactions among the hub genes. The two hub genes CD96 and TBX21 showed close correlation with the survival of ovarian cancer patients. The sequencing results of SKOV3 cells transfected with miR-4772 mimics or inhibitor showed that the changes in miR-4772 expression level caused obvious changes in the expressions of the 12 hub genes and PD-L1. CONCLUSION: MiR-4772 plays a regulatory role in the formation of tumor immune microenvironment in ovarian cancer by regulating 12 hub genes.

How much space of the spinal canal should be restored by hoisting the vertebrae-OPLL complex for sufficient decompression in anterior controllable antedisplacement and fusion? A multicenter clinical radiological study.

BACKGROUND CONTEXT: Anterior controllable antedisplacement and fusion (ACAF) is a novel surgical technique for the treatment of ossification of the posterior longitudinal ligament (OPLL). Its prognostic factors for decompression have not been well studied. Additionally, no detailed radiological standard has been set for hoisting the vertebrae-OPLL complex (VOC) in ACAF. PURPOSE: To identify the possible prognostic factors for decompression outcomes after ACAF for cervical OPLL, to determine the critical value of radiological parameters for predicting good outcomes, and to establish a radiological standard for hoisting the VOC in ACAF. STUDY DESIGN: This was a retrospective multicenter study. PATIENT SAMPLE: A total of 121 consecutive patients with OPLL who underwent ACAF at a point between January 2017 and June 2018 at any one of seven facilities and were monitored for at least 1 year afterward were enrolled in a multicenter study. OUTCOME MEASURES: Japanese Orthopedic Association (JOA) scores, recovery rate (RR) of neurologic function, and surgical complications were used to determine the effectiveness of ACAF. METHODS: Patients were divided into two groups according to their RR for neurologic function. Patients with an RR of ≥50% and an RR of <50% were designated as having good and poor decompression outcomes, respectively. The relationship between various possible prognostic factors and decompression outcomes was assessed by univariate and multivariate analysis. The receiver operating characteristic curve was used to determine the optimal cutoff value of the radiological parameters for prediction of good decompression outcomes. Next, the patients were redivided into three groups according to the cutoff value of the selected radiological parameter (postoperative anteroposterior canal diameter [APD] ratio). Patients with postoperative APD ratios of ≤80.7%, 80.7%-100%, and ≥100% were defined as members of the incomplete, optimal, and excessive antedisplacement groups, respectively. Differences in decompression outcomes among the three groups were compared to verify the reliability of the postoperative APD ratio and assess the necessity of excessive antedisplacement. RESULTS: Multivariate logistic regression analysis showed that patients' age at surgery (odds ratio [OR]=1.18; 95% confidence interval [CI]=1.08-1.29; p<.01) and postoperative APD ratio (OR=0.83; 95% CI=0.77-0.90; p<.01) were independently associated with decompression outcomes. The optimal cutoff point of the postoperative APD ratio was calculated at 80.7%, with 86.2% sensitivity and 73.5% specificity. There were no significant differences in the postoperative JOA scores and RRs between the excessive antedisplacement group and optimal antedisplacement group (p>.05). However, a lower incidence of cerebrospinal fluid leakage and screw slippage was observed in the optimal antedisplacement group (p<.05). CONCLUSIONS: Patients' age at surgery and their postoperative APD ratio are the two prognostic factors of decompression outcomes after ACAF. The postoperative APD ratio is also the most accurate radiological parameter for predicting good outcomes. Our findings suggest that it is essential for neurologic recovery to restore the spinal canal to more than 80.7% of its original size (postoperative APD ratio >80.7%), and restoration to less than its original size (postoperative APD ratio <100%) will help reduce the incidence of surgical complications. This may serve as a valuable reference for establishment of a radiological standard for hoisting the VOC in ACAF.

Bootstrap confidence intervals for the optimal cutoff point to bisect estimated probabilities from logistic regression.

To classify estimated probabilities from a logistic regression model into two groups (e.g., yes or no, disease or no disease), the optimal cutoff point or threshold is crucial. While various methods have been proposed for estimating such a threshold, statistical inference is not generally available. To tackle this issue, we put forward several bootstrap based methods, including the conventional nonparametric bootstrap standard errors and the quantile interval. Special emphasis is placed on a more precise bagging estimator of the optimal cutoff point, for which a confidence interval can be obtained via the recently proposed infinitesimal jackknife method. We investigate the empirical performance of the proposed methods by simulation and illustrate their use via the analysis of a fertility data set concerning seminal quality prediction.

CPMCGLM: an R package for p-value adjustment when looking for an optimal transformation of a single explanatory variable in generalized linear models.

BACKGROUND: In medical research, explanatory continuous variables are frequently transformed or converted into categorical variables. If the coding is unknown, many tests can be used to identify the "optimal" transformation. This common process, involving the problems of multiple testing, requires a correction of the significance level. Liquet and Commenges proposed an asymptotic correction of significance level in the context of generalized linear models (GLM) (Liquet and Commenges, Stat Probab Lett 71:33-38, 2005). This procedure has been developed for dichotomous and Box-Cox transformations. Furthermore, Liquet and Riou suggested the use of resampling methods to estimate the significance level for transformations into categorical variables with more than two levels (Liquet and Riou, BMC Med Res Methodol 13:75, 2013). RESULTS: CPMCGLM provides to users both methods of p-value adjustment. Futhermore, they are available for a large set of transformations. This paper aims to provide insight the user an overview of the methodological context, and explain in detail the use of the CPMCGLM R package through its application to a real epidemiological dataset. CONCLUSION: We present here the CPMCGLMR package providing efficient methods for the correction of type-I error rate in the context of generalized linear models. This is the first and the only available package in R providing such methods applied to this context. This package is designed to help researchers, who work principally in the field of biostatistics and epidemiology, to analyze their data in the context of optimal cutoff point determination.

Statistical Methodology for Multiclass Classifications: Applications to Dementia.

Alzheimer's disease (AD) is a common, devastating disease which carries a heavy economic burden. Accelerated efforts to identify presymptomatic stages of AD and biomarkers to classify the disease are urgent needs. Currently, no biomarkers can perfectly discriminate individuals into multiple disease categories of AD (no cognitive impairment, mild cognitive impairment, and dementia). Although many biomarkers for diagnosis and their various features are being studied, we lack advanced statistical methods which can fully utilize biomarkers to classify AD accurately, thereby facilitating evaluation of putative markers both alone and in combination. In this paper, we propose two approaches: 1) a forward addition procedure in which we adapt an additive logistic regression model to the setting for disease with ordered multiple categories. Using this approach, we select and combine multiple cross-sectional biomarkers to improve diagnostic accuracy, and 2) a method by extending the Neyman-Pearson Lemma to the ordered three disease categories to construct optimal cutoff points to distinguish multiple disease categories. We evaluate the robustness of the proposed model using a simulation study. Then we apply these two methods to data from the Religious Orders Study to examine the feasibility of combining biomarkers, and compare the diagnostic accuracy between the proposed methods and existing methods including model-based methods (ordinal logistic regression and quadratic discriminant analysis), a tree-based method CART, and the Youden index method. The two proposed methods facilitate evaluations of biomarkers for conditions with graded, rather than binary, classifications. The evaluation of the performance of different approaches provides guidance of how to choose approaches to address research questions.

Clinically significant radiographic parameter for thoracic myelopathy caused by ossification of the ligamentum flavum.

PURPOSE: To investigate radiographic parameters to improve the accuracy of radiologic diagnosis for ossification of ligamentum flavum (OLF)-induced thoracic myelopathy and thereby establish a useful diagnostic method for identifying the responsible segment. METHODS: We classified 101 patients who underwent surgical treatment for OLF-induced thoracic myelopathy as the myelopathy group and 102 patients who had incidental OLF and were hospitalized with compression fracture as the non-myelopathy group between January 2009 and December 2016. We measured the thickness of OLF (TOLF), cross-sectional area of OLF (AOLF), anteroposterior canal diameter, and the ratio of each of these parameters. RESULTS: Most OLF cases with lateral-type axial morphology were in the non-myelopathy group and most with fused and tuberous type in the myelopathy group. Most grade-I and grade-II cases were also in the non-myelopathy group, whereas grade-IV cases were mostly observed in the myelopathy group. The AOLF ratio was found to be the best radiologic parameter. The optimal cutoff point of the AOLF ratio was 33.00%, with 87.1% sensitivity and 87.3% specificity. The AOLF ratio was significantly correlated with preoperative neurological status. CONCLUSIONS: An AOLF ratio greater than 33% is the most accurate diagnostic indicator of OLF-induced thoracic myelopathy. In cases of multiple-segment OLF, confirmation of cord signal change on MRI and an AOLF measurement will help determine the responsible segment. AOLF measurement will also improve the accuracy of diagnosis of OLF-induced thoracic myelopathy in cases of grade III or extended-type axial morphology. These slides can be retrieved under Electronic Supplementary Material.

A Validity and Reliability Study of the Japanese Version of the Geriatric Depression Scale 15 (GDS-15-J).

OBJECTIVE: The 15-item Geriatric Depression Scale (GDS-15) is one of the most widely used screening instruments for depression among the elderly. The aim of this study was to examine the validity and reliability of the Japanese version of the GDS-15 (GDS-15-J) in comparison with the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR) criteria for depression. METHODS: The study participants were 128 elderly outpatients (age range, 55 to 92 years) categorized into two groups (76 non-depressive patients, 52 depressive patients) based on the DSM-IV-TR criteria for depression. RESULTS: Logistic regression analysis showed that regardless of age and sex, the GDS-15-J score could be used to screen patients for depression (p < .001). The validity of the GDS-15-J for depression assessed against DSM-IV-TR criteria was excellent based on receiver operating characteristic analysis (optimal cutoff point: 6/7; sensitivity: .98; specificity: .86). The recommended optimal cutoff score when screening for depression is 6/7. To evaluate the constructive validity of the GDS-15-J, factor analysis was performed. Three factors were extracted. Cronbach's alpha reliability coefficient was .83 to the GDS-15-J scale, which indicated a high degree of internal consistency. CONCLUSION: The GDS-15-J is a clinically applicable screening instrument for depression. CLINICAL IMPLICATIONS: In this study this version of the GDS-15-J displayed excellent psychometric properties using a 6/7 cut off. Analyses suggest some items that might be removed in future studies of an abbreviated scale.

Optimal cut points for quality of life questionnaire-core 30 (QLQ-C30) scales: utility for clinical trials and updates of prognostic systems in advanced hepatocellular carcinoma.

BACKGROUND: Health-related quality of life (QoL) has been validated as a prognostic factor for cancer patients; however, to be used in routine practice, QoL scores must be dichotomized. Cutoff points are usually based on arbitrary percentile values. We aimed to identify optimal cutoff points for six QoL scales and to quantify their added utility in the performance of four prognostic classifications in patients with hepatocellular carcinoma (HCC). METHODS: We reanalyzed data of 271 patients with advanced HCC recruited between July 2002 and October 2003 from 79 institutions in France in the CHOC trial, designed to assess the efficacy of long-acting octreotide. QoL was assessed with the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (QLQ-C30). The scores ranged from 0 to 100. Identification of optimal cutoff points was based on the method of Faraggi and Simon [Stat Med 1996;15:2203-2213]. Improvement in the performance of prognostic classifications was studied with Harrell's C-index, the net reclassification improvement (NRI), and integrated discrimination improvement (IDI). RESULTS: We found that optimal cutoff points were 50 for global health, 58.33 for physical functioning, 66.67 for role functioning, 66.67 for fatigue, 0 for dyspnea, and 33.33 for diarrhea. The addition of QoL and clinical factors improved the performance of all four prognostic classifications, with improvement in the range of 0.02-0.09 for the C-index, 0.24-0.78 for 3-month NRI, and 0.02-0.10 for IDI. CONCLUSION: These cutoff values for QoL scales can be useful to identify HCC patients with very poor prognosis and thus improve design of clinical trials and treatment adjustment for these patients.