Development of a preoperative prediction model for new-onset diabetes mellitus after partial pancreatectomy

ABSTRACT: Pancreatectomy is an invasive surgery that is sometimes associated with complications. New-onset diabetes mellitus sometimes develops after partial pancreatectomy and severely affects the patient's quality of life. This study aimed to develop a preoperative prediction model of new-onset diabetes mellitus after partial pancreatectomy, which will help patients and surgeons to achieve more easily better common decisions on regarding whether to perform partial pancreatectomy. This retrospective cohort study analyzed medical records of patients who underwent partial pancreatectomy (total pancreatectomy excluded) from April 1, 2008, to February 28, 2016, which were available in the database provided by Medical Data Vision Co., Ltd. (Tokyo, Japan). The predictors were preoperative age, body mass index, hemoglobin A1c level, blood glucose level, and indication for partial pancreatectomy. The outcome was the development of new-onset diabetes mellitus at 1 to 12 months after partial pancreatectomy. We used a logistic regression model and calculated the scores of each predictor. To determine test performance, we assessed discrimination ability using the receiver operating characteristic curve and calibration with a calibration plot and the Hosmer-Lemeshow test. We also performed internal validation using the bootstrap method. Of 681 patients, 125 (18.4%) had new-onset diabetes mellitus after partial pancreatectomy. The developed prediction model had a possible range of 0 to 46 points. The median score was 13, and the interquartile range was 9 to 22. The C-statistics of the receiver operating characteristic curve on the score to predict the outcome was .70 (95% confidence interval [CI], .65-.75). Regarding the test performance, the Hosmer-Lemeshow test was not significant (P = .17), and calibration was good. In the bootstrapped cohorts, the C-statistics was .69 (95% CI, .62-.76). We developed a preoperative prediction model for new-onset diabetes mellitus after partial pancreatectomy. This would provide important information for surgeons and patients when deciding whether to perform partial pancreatectomy

Grading Meningioma : A Comparative Study of Thallium-SPECT and FDG-PET

The purpose was to compare capability of fluorine-18 fluorodeoxyglucose (FDG)-PET and thallium-201 (Tl)-SPECT for grading meningioma. This retrospective study was conducted as a case-control study under approval by the institutional review board. In the hospital information system, 67 patients (22 men and 45 women) who had both FDG-PET and Tl-SPECT preoperative examinations were found with histopathologic diagnosis of meningioma. The maximum FDG uptake values of the tumors were measured, and they were standardized to the whole body (SUVmax) and normalized as gray matter ratio (SUVRmax). Mean and maximum Tl uptake ratios (TURmean and TURmax, respectively) of the tumors were measured and normalized as ratios to those of the contralateral normal brain. Receiver-operating characteristic curve analyses of the 4 indexes were conducted for differentiation between low- and high-grade meningiomas, and areas under the curves (AUCs) were compared. Correlation coefficients were calculated between these indexes and Ki-67. Fifty-six meningiomas were classified as grade I (low grade), and 11 were grade II or III (high grade). In all 4 indexes, a significant difference was observed between low- and high-grade meningiomas (P<0.05). AUCs were 0.817 (SUVmax), 0.781 (SUVRmax), 0.810 (TURmean), and 0.831 (TURmax), and no significant difference was observed among the indexes. Their sensitivity and specificity were 72.7% to 90.9% and 71.4% to 87.5%, respectively. Correlation of the 4 indexes to Ki-67 was statistically significant, but coefficients were relatively low (0.273-0.355). Tl-SPECT, which can be used at hospitals without a cyclotron or an FDG distribution network, has high diagnostic capability of meningioma grades comparable to FDG-PET

Review Article : Feudalism or Absolute Monarchism?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68809/2/10.1177_009770049001600304.pd

Evaluation of Complexity Measurement Tools for Correlations with Health-Related Outcomes, Health Care Costs and Impacts on Healthcare Providers: A Scoping Review

Various tools to measure patient complexity have been developed. Primary care physicians often deal with patient complexity. However, their usefulness in primary care settings is unclear. This study explored complexity measurement tools in general adult and patient populations to investigate the correlations between patient complexity and outcomes, including health-related patient outcomes, healthcare costs, and impacts on healthcare providers. We used a five-stage scoping review framework, searching MEDLINE and CINAHL, including reference lists of identified studies. A total of 21 patient complexity management tools were found. Twenty-five studies examined the correlation between patient complexity and health-related patient outcomes, two examined healthcare costs, and one assessed impacts on healthcare providers. No studies have considered sharing information or action plans with multidisciplinary teams while measuring outcomes for complex patients. Of the tools, eleven used face-to-face interviews, seven extracted data from medical records, and three used self-assessments. The evidence of correlations between patient complexity and outcomes was insufficient for clinical implementation. Self-assessment tools might be convenient for conducting further studies. A multidisciplinary approach is essential to develop effective intervention protocols. Further research is required to determine these correlations in primary care settings