The role of frailty in geriatric cranial neurosurgery for primary central nervous system neoplasms

OBJECTIVE. Frailty is a clinical state of increased vulnerability due to age-associated decline and has been well established as a perioperative risk factor. Geriatric patients have a higher risk of frailty, higher incidence of brain cancer, and increased postoperative complication rates compared to nongeriatric patients. Yet, literature describing the effects of frailty on short- and long-term complications in geriatric patients is limited. In this study, the authors evaluate the effects of frailty in geriatric patients receiving cranial neurosurgery for a primary CNS neoplasm. METHODS. The authors conducted a retrospective cohort study of geriatric patients receiving cranial neurosurgery for a primary CNS neoplasm between 2010 and 2017 by using the Nationwide Readmission Database. Demographics and frailty were queried at primary admission, and readmissions were analyzed at 30-, 90-, and 180-day intervals. Complications of interest included infection, anemia, infarction, kidney injury, CSF leak, urinary tract infection, and mortality. Nearest-neighbor propensity score matching for demographics was implemented to identify nonfrail control patients with similar diagnoses and procedures. The analysis used Welch two-sample t-tests for continuous variables and chi-square test with odds ratios. RESULTS. A total of 6713 frail patients and 6629 nonfrail patients were identified at primary admission. At primary admission, frail geriatric patients undergoing cranial neurosurgery had increased odds of developing acute posthemorrhagic anemia (OR 1.56, 95% CI 1.23–1.98; p = 0.00020); acute infection (OR 3.16, 95% CI 1.70–6.36; p = 0.00022); acute kidney injury (OR 1.32, 95% CI 1.07–1.62; p = 0.0088); urinary tract infection prior to discharge (OR 1.97, 95% CI 1.71–2.29; p < 0.0001); acute postoperative cerebral infarction (OR 1.57, 95% CI 1.17–2.11; p = 0.0026); and mortality (OR 1.64, 95% CI 1.22–2.24; p = 0.0012) compared to nonfrail geriatric patients receiving the same procedure. In addition, frail patients had a significantly increased inpatient length of stay (p < 0.0001) and all-payer hospital cost (p < 0.0001) compared to nonfrail patients at the time of primary admission. However, no significant difference was found between frail and nonfrail patients with regard to rates of infection, thromboembolism, CSF leak, dural tear, cerebral infarction, acute kidney injury, and mortality at all readmission time points. CONCLUSIONS. Frailty may significantly increase the risks of short-term acute complications in geriatric patients receiving cranial neurosurgery for a primary CNS neoplasm. Long-term analysis revealed no significant difference in complications between frail and nonfrail patients. Further research is warranted to understand the effects and timeline of frailty in geriatric patients

Characterizing blood metabolomics profiles associated with self-reported food intakes in female twins

Using dietary biomarkers in nutritional epidemiological studies may better capture exposure and improve the level at which diet-disease associations can be established and explored. Here, we aimed to identify and evaluate reproducibility of novel biomarkers of reported habitual food intake using targeted and non-targeted metabolomic blood profiling in a large twin cohort. Reported intakes of 71 food groups, determined by FFQ, were assessed against 601 fasting blood metabolites in over 3500 adult female twins from the TwinsUK cohort. For each metabolite, linear regression analysis was undertaken in the discovery group (excluding MZ twin pairs discordant [≥1 SD apart] for food group intake) with each food group as a predictor adjusting for age, batch effects, BMI, family relatedness and multiple testing (1.17x10-6 = 0.05/[71 food groups x 601 detected metabolites]). Significant results were then replicated (non-targeted: P<0.05; targeted: same direction) in the MZ discordant twin group and results from both analyses meta-analyzed. We identified and replicated 180 significant associations with 39 food groups (P<1.17x10-6), overall consisting of 106 different metabolites (74 known and 32 unknown), including 73 novel associations. In particular we identified trans-4-hydroxyproline as a potential marker of red meat intake (0.075[0.009]; P = 1.08x10-17), ergothioneine as a marker of mushroom consumption (0.181[0.019]; P = 5.93x10-22), and three potential markers of fruit consumption (top association: apple and pears): including metabolites derived from gut bacterial transformation of phenolic compounds, 3-phenylpropionate (0.024[0.004]; P = 1.24x10-8) and indolepropionate (0.026[0.004]; P = 2.39x10-9), and threitol (0.033[0.003]; P = 1.69x10-21). With the largest nutritional metabolomics dataset to date, we have identified 73 novel candidate biomarkers of food intake for potential use in nutritional epidemiological studies. We compiled our findings into the DietMetab database (http://www.twinsuk.ac.uk/dietmetab-data/), an online tool to investigate our top associations

Cemented versus Cementless Femoral Fixation for Elective Primary Total Hip Arthroplasty: A Nationwide Analysis of Short-Term Complication and Readmission Rates

Cementless fixation during total hip arthroplasty (THA) is the predominant mode of fixation utilized for both acetabular and femoral components during elective primary THAs performed in the United States. This study aims to compare early complication and readmission rates between primary THA patients receiving cemented versus cementless femoral fixation. The 2016–2017 National Readmissions Database was queried to identify patients undergoing elective primary THA. Postoperative complication and readmission rates at 30, 90, and 180 days were compared between cemented and cementless cohorts. Univariate analysis was conducted to compare differences between cohorts. Multivariate analysis was performed to account for confounding variables. Of 447,902 patients, 35,226 (7.9%) received cemented femoral fixation, while 412,676 (92.1%) did not. The cemented group was older (70.0 vs. 64.8, p p p p p p p = 0.002). Cemented femoral fixation was associated with significantly fewer short-term periprosthetic fractures, but more unplanned readmissions, deaths, and postoperative complications compared to cementless femoral fixation in patients undergoing elective THA

Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification.

Background: Bone marrow fat (BMF) fraction quantification in vertebral bodies is used as a novel imaging biomarker to assess and characterize chronic lower back pain. However, manual segmentation of vertebral bodies is time consuming and laborious. Purpose: (1) Develop a deep learning pipeline for segmentation of vertebral bodies using quantitative water-fat MRI. (2) Compare BMF measurements between manual and automatic segmentation methods to assess performance. Materials and Methods: In this retrospective study, MR images using a 3D spoiled gradient-recalled echo (SPGR) sequence with Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) reconstruction algorithm were obtained in 57 subjects (28 women, 29 men, mean age, 47.2 ± 12.6 years). An artificial network was trained for 100 epochs on a total of 165 lumbar vertebrae manually segmented from 31 subjects. Performance was assessed by analyzing the receiver operating characteristic curve, precision-recall, F1 scores, specificity, sensitivity, and similarity metrics. Bland-Altman analysis was used to assess performance of BMF fraction quantification using the predicted segmentations. Results: The deep learning segmentation method achieved an AUC of 0.92 (CI 95%: 0.9186, 0.9195) on a testing dataset (n = 24 subjects) on classification of pixels as vertebrae. A sensitivity of 0.99 and specificity of 0.80 were achieved for a testing dataset, and a mean Dice similarity coefficient of 0.849 ± 0.091. Comparing manual and automatic segmentations on fat fraction maps of lumbar vertebrae (n = 124 vertebral bodies) using Bland-Altman analysis resulted in a bias of only -0.605% (CI 95% = -0.847 to -0.363%) and agreement limits of -3.275% and +2.065%. Automatic segmentation was also feasible in 16 ± 1 s. Conclusion: Our results have demonstrated the feasibility of automated segmentation of vertebral bodies using deep learning models on water-fat MR (Dixon) images to define vertebral regions of interest with high specificity. These regions of interest can then be used to quantify BMF with comparable results as manual segmentation, providing a framework for completely automated investigation of vertebral changes in CLBP

Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification.

Background: Bone marrow fat (BMF) fraction quantification in vertebral bodies is used as a novel imaging biomarker to assess and characterize chronic lower back pain. However, manual segmentation of vertebral bodies is time consuming and laborious. Purpose: (1) Develop a deep learning pipeline for segmentation of vertebral bodies using quantitative water-fat MRI. (2) Compare BMF measurements between manual and automatic segmentation methods to assess performance. Materials and Methods: In this retrospective study, MR images using a 3D spoiled gradient-recalled echo (SPGR) sequence with Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL) reconstruction algorithm were obtained in 57 subjects (28 women, 29 men, mean age, 47.2 ± 12.6 years). An artificial network was trained for 100 epochs on a total of 165 lumbar vertebrae manually segmented from 31 subjects. Performance was assessed by analyzing the receiver operating characteristic curve, precision-recall, F1 scores, specificity, sensitivity, and similarity metrics. Bland-Altman analysis was used to assess performance of BMF fraction quantification using the predicted segmentations. Results: The deep learning segmentation method achieved an AUC of 0.92 (CI 95%: 0.9186, 0.9195) on a testing dataset (n = 24 subjects) on classification of pixels as vertebrae. A sensitivity of 0.99 and specificity of 0.80 were achieved for a testing dataset, and a mean Dice similarity coefficient of 0.849 ± 0.091. Comparing manual and automatic segmentations on fat fraction maps of lumbar vertebrae (n = 124 vertebral bodies) using Bland-Altman analysis resulted in a bias of only -0.605% (CI 95% = -0.847 to -0.363%) and agreement limits of -3.275% and +2.065%. Automatic segmentation was also feasible in 16 ± 1 s. Conclusion: Our results have demonstrated the feasibility of automated segmentation of vertebral bodies using deep learning models on water-fat MR (Dixon) images to define vertebral regions of interest with high specificity. These regions of interest can then be used to quantify BMF with comparable results as manual segmentation, providing a framework for completely automated investigation of vertebral changes in CLBP

Influence of patient‐specific factors when comparing multifidus fat infiltration between chronic low back pain patients and asymptomatic controls

Abstract Introduction Many studies have attempted to link multifidus (MF) fat infiltration with muscle quality and chronic low back pain (cLBP), but there is no consensus on these relationships. Methods In this cross‐sectional cohort study, 39 cLBP patients and 18 asymptomatic controls were included. The MF muscle was manually segmented at each lumbar disc level and fat fraction (FF) measurements were taken from the corresponding advanced imaging water‐fat images. We assessed the distribution patterns of MF fat from L1L2 to L5S1 and compared these patterns between groups. The sample was stratified by age, sex, body mass index (BMI), subject‐reported pain intensity (VAS), and subject‐reported low back pain disability (oswestry disability index, ODI). Results Older patients had significantly different MF FF distribution patterns compared to older controls (p < 0.0001). Male patients had 34.8% higher mean lumbar spine MF FF compared to male controls (p = 0.0006), significantly different MF FF distribution patterns (p = 0.028), 53.7% higher mean MF FF measurements at L2L3 (p = 0.037), and 50.6% higher mean MF FF measurements at L3L4 (p = 0.041). Low BMI patients had 29.7% higher mean lumbar spine MF FF compared to low BMI controls (p = 0.0077). High BMI patients only had 4% higher mean lumbar spine MF FF compared to high BMI controls (p = 0.7933). However, high BMI patients had significantly different MF FF distribution patterns compared to high BMI controls (p = 0.0324). Low VAS patients did not significantly differ from the control cohort for any of our outcomes of interest; however, high VAS patients had 24.3% higher mean lumbar spine MF FF values (p = 0.0011), significantly different MF FF distribution patterns (p < 0.0001), 34.7% higher mean MF FF at L2L3 (p = 0.040), and 34.6% higher mean MF FF at L3L4 (p = 0.040) compared to the control cohort. Similar trends were observed for ODI. Conclusions This study suggests that when the presence of paraspinal muscle fat infiltration is not characteristic of an individual's age, sex, and BMI, it may be associated with lower back pain