Dataset Information

Intra-individual association between C-reactive protein and insulin administration in postoperative lumbar spinal canal stenosis patients: A retrospective cohort study.

ABSTRACT: The association of intra-individual variability in insulin requirements with C-reactive protein levels among acute phase patients remains unclear. This retrospective cohort study aimed to evaluate this association. Patients with type 2 diabetes undergoing surgery for lumbar spinal canal stenosis were included in the study. We analyzed 286 records of 49 patients using the linear mixed effects model. The model showed C-reactive protein levels to be significantly associated with insulin requirements, with an effect size of 0.60 U/day for an elevation of 1 mg/dL. The effect size was increased in patients with higher hemoglobin A1c levels. Our findings imply that C-reactive protein levels could be a useful clinical biomarker when blood glucose levels are controlled in acute phase patients.

SUBMITTER: Kurisu K

PROVIDER: S-EPMC7378432 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Intra-individual association between C-reactive protein and insulin administration in postoperative lumbar spinal canal stenosis patients: A retrospective cohort study.

Kurisu Ken K Tsurutani Yuya Y Inoue Kosuke K Hoshino Yoshitomo Y Saiki Fumiko F Yoshiuchi Kazuhiro K

Journal of diabetes investigation 20200209 4

The association of intra-individual variability in insulin requirements with C-reactive protein levels among acute phase patients remains unclear. This retrospective cohort study aimed to evaluate this association. Patients with type 2 diabetes undergoing surgery for lumbar spinal canal stenosis were included in the study. We analyzed 286 records of 49 patients using the linear mixed effects model. The model showed C-reactive protein levels to be significantly associated with insulin requirement ...[more]

PMID: 31912618

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Project description:BackgroundThe accurate preoperative identification of decompression levels is crucial for the success of surgery in patients with multi-level lumbar spinal stenosis (LSS). The objective of this study was to develop machine learning (ML) classifiers that can predict decompression levels using computed tomography myelography (CTM) data from LSS patients.MethodsA total of 1095 lumbar levels from 219 patients were included in this study. The bony spinal canal in CTM images was manually delineated, and radiomic features were extracted. The extracted data were randomly divided into training and testing datasets (8:2). Six feature selection methods combined with 12 ML algorithms were employed, resulting in a total of 72 ML classifiers. The main evaluation indicator for all classifiers was the area under the curve of the receiver operating characteristic (ROC-AUC), with the precision-recall AUC (PR-AUC) serving as the secondary indicator. The prediction outcome of ML classifiers was decompression level or not.ResultsThe embedding linear support vector (embeddingLSVC) was the optimal feature selection method. The feature importance analysis revealed the top 5 important features of the 15 radiomic predictors, which included 2 texture features, 2 first-order intensity features, and 1 shape feature. Except for shape features, these features might be eye-discernible but hardly quantified. The top two ML classifiers were embeddingLSVC combined with support vector machine (EmbeddingLSVC_SVM) and embeddingLSVC combined with gradient boosting (EmbeddingLSVC_GradientBoost). These classifiers achieved ROC-AUCs over 0.90 and PR-AUCs over 0.80 in independent testing among the 72 classifiers. Further comparisons indicated that EmbeddingLSVC_SVM appeared to be the optimal classifier, demonstrating superior discrimination ability, slight advantages in the Brier scores on the calibration curve, and Net benefits on the Decision Curve Analysis.ConclusionsML successfully extracted valuable and interpretable radiomic features from the spinal canal using CTM images, and accurately predicted decompression levels for LSS patients. The EmbeddingLSVC_SVM classifier has the potential to assist surgical decision making in clinical practice, as it showed high discrimination, advantageous calibration, and competitive utility in selecting decompression levels in LSS patients using canal radiomic features from CTM.

Project description:PurposeTo develop a self-administered diagnostic screening questionnaire for lumbar spinal stenosis (LSS) consisting of items with high content validity and to investigate the diagnostic value of the questionnaire and the items.Patients and methodsA self-reported diagnostic LSS screening questionnaire was developed based on items from the existing literature describing key symptoms of LSS. The screening questionnaire (index test) was to be tested in a cohort of patients with persistent lumbar and/or leg pain recruited from a Danish publicly funded outpatient secondary care spine clinic with clinicians performing the reference test. However, to avoid unnecessary collection of data if the screening questionnaire proved to be of limited value, a case-control design was incorporated into the cohort design including an interim analysis. Additional cases for the case-control study were recruited at two Danish publicly funded spine surgery departments. Prevalence, sensitivity, specificity and diagnostic odds ratio (OR) were calculated for each individual item, and AUC (area under the curve) was calculated to examine the performance of the full questionnaire.ResultsA 13-item Danish questionnaire was developed and tested in 153 cases and 230 controls. The interim analysis was not in favour of continuing the cohort study, and therefore, only results from the case-control study are reported. There was a positive association for all items except the presence of back pain. However, the association was only moderate with ORs up to 3.3. When testing the performance of the whole questionnaire, an AUC of 0.72 was reached with a specificity of 20% for a fixed sensitivity of 95%.ConclusionThe items were associated with LSS and therefore have some potential to identify LSS patients. However, the association was not strong enough to provide sufficient accuracy for a diagnostic tool. Additional dimensions of symptoms of LSS need identification to obtain a reliable questionnaire for screening purposes.

Dataset Information

Intra-individual association between C-reactive protein and insulin administration in postoperative lumbar spinal canal stenosis patients: A retrospective cohort study.

Publications

Intra-individual association between C-reactive protein and insulin administration in postoperative lumbar spinal canal stenosis patients: A retrospective cohort study.

Similar Datasets

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