How to Craft a Powerful Annual Report

To an oversight agency, an annual report is more than a financial statement. At the Office of the Inspector General (OIG) for the City of Philadelphia, we view our annual report as an opportunity to increase awareness of our office, to encourage public reporting, and to deter wrongdoing. Because most investigations originate from tips submitted by citizens, we see community engagement as critical to our success. In our years of report writing, we have gained a lot of experience about what works and what does not work, and we have distilled that experience into six basic lessons for maximizing the impact of an annual report

How to Craft a Powerful Annual Report

To an oversight agency, an annual report is more than a financial statement. At the Office of the Inspector General (OIG) for the City of Philadelphia, we view our annual report as an opportunity to increase awareness of our office, to encourage public reporting, and to deter wrongdoing. Because most investigations originate from tips submitted by citizens, we see community engagement as critical to our success. In our years of report writing, we have gained a lot of experience about what works and what does not work, and we have distilled that experience into six basic lessons for maximizing the impact of an annual report

Predicting mortality among septic patients presenting to the emergency department–a cross sectional analysis using machine learning

Background Sepsis is a life-threatening condition, causing almost one fifth of all deaths worldwide. The aim of the current study was to identify variables predictive of 7- and 30-day mortality among variables reflective of the presentation of septic patients arriving to the emergency department (ED) using machine learning. Methods Retrospective cross-sectional design, including all patients arriving to the ED at Södersjukhuset in Sweden during 2013 and discharged with an International Classification of Diseases (ICD)-10 code corresponding to sepsis. All predictions were made using a Balanced Random Forest Classifier and 91 variables reflecting ED presentation. An exhaustive search was used to remove unnecessary variables in the final model. A 10-fold cross validation was performed and the accuracy was described using the mean value of the following: AUC, sensitivity, specificity, PPV, NPV, positive LR and negative LR. Results The study population included 445 septic patients, randomised to a training (n = 356, 80%) and a validation set (n = 89, 20%). The six most important variables for predicting 7-day mortality were: “fever”, “abnormal verbal response”, “low saturation”, “arrival by emergency medical services (EMS)”, “abnormal behaviour or level of consciousness” and “chills”. The model including these variables had an AUC of 0.83 (95% CI: 0.80–0.86). The final model predicting 30-day mortality used similar six variables, however, including “breathing difficulties” instead of “abnormal behaviour or level of consciousness”. This model achieved an AUC = 0.80 (CI 95%, 0.78–0.82). Conclusions The results suggest that six specific variables were predictive of 7- and 30-day mortality with good accuracy which suggests that these symptoms, observations and mode of arrival may be important components to include along with vital signs in a future prediction tool of mortality among septic patients presenting to the ED. In addition, the Random Forests appears to be a suitable machine learning method on which to build future studies

Appropriate Use Criteria for Estrogen Receptor-Targeted PET Imaging with 16α-18F-Fluoro-17β-Fluoroestradiol.

PET imaging with 16α- 18F-fluoro-17β-fluoroestradiol ( 18F-FES), a radiolabeled form of estradiol, allows whole-body, noninvasive evaluation of estrogen receptor (ER). 18F-FES is approved by the U.S. Food and Drug Administration as a diagnostic agent "for the detection of ER-positive lesions as an adjunct to biopsy in patients with recurrent or metastatic breast cancer." The Society of Nuclear Medicine and Molecular Imaging (SNMMI) convened an expert work group to comprehensively review the published literature for 18F-FES PET in patients with ER-positive breast cancer and to establish appropriate use criteria (AUC). The findings and discussions of the SNMMI 18F-FES work group, including example clinical scenarios, were published in full in 2022 and are available at https://www.snmmi.org/auc Of the clinical scenarios evaluated, the work group concluded that the most appropriate uses of 18F-FES PET are to assess ER functionality when endocrine therapy is considered either at initial diagnosis of metastatic breast cancer or after progression of disease on endocrine therapy, the ER status of lesions that are difficult or dangerous to biopsy, and the ER status of lesions when other tests are inconclusive. These AUC are intended to enable appropriate clinical use of 18F-FES PET, more efficient approval of FES use by payers, and promotion of investigation into areas requiring further research. This summary includes the rationale, methodology, and main findings of the work group and refers the reader to the complete AUC document