Histopathological Evaluation of Myometrial Lesions of the Uterus in Nnewi Teaching Hospital: (Five‑Year Retrospective Study)

Introduction: A 5‑year retrospective study to evaluate the lesions of myometrium (both nonneoplastic and neoplastic) in the hysterectomyand myomectomy specimens received in our institution. Aim: This research will serve as a baseline study of different myometrial lesions in the histopathology department of Nnamdi Azikiwe University Teaching Hospital (NAUTH) Nnewi. This is the first of such a study since the institution of the department. The study will also highlight myometrial lesions in relation to the age and mode of presentations as well as histopathological features. Methodology: The pathology report forms in the histopathology department NAUTH, Nnewi, were retrieved, and relevant information was extracted. Atotal of 290 cases of myometrial lesions were obtained within the study period, of which 283 cases that fulfilled the inclusion criteria were analyzed. The processed tissues and the slides stained with regular histochemical stain (hematoxylin and eosin) technique in this 5‑year study period were reviewed by the above researchers using multi‑headed microscope (®CARL ZEISS). Results: The myometrial lesions observed include leiomyoma, leiomyomata, leiomyosarcoma, leiomyoma coexisting with adenomyosis, adenomyosis, invasive carcinosarcoma, and hemorrhagic necrosis following uterine rupture. The age range at the presentation was between 10and 80 years. The mean age for leiomyoma was 39.24 ± 8.41 standard deviation (SD), whereas the mean age for adenomyosis was 43 ± 9.86 SD.Leiomyoma was the most common myometrial lesion with a frequency of 93.9% (266 cases) and show degenerative changes in 139 cases (52.%)Followed by coexisting leiomyoma with adenomyosis which had a frequency of 3.9% (11 cases). Atotal of 184 leiomyoma cases with a frequencyof 69.2% occur in multiple nodules. Adenomyosis alone had a frequency of 3.18% (9 cases). Therefore, the total number of adenomyosis inthis research was 20 cases. Menorrhagia was the most common clinical symptoms with a frequency of 31.4% (82 cases). Leiomyosarcomahad a frequency of 1.77% (5 cases), whereas the least represented were hemorrhagic necrosis and invasive carcinosarcoma with frequenciesof 2 (0.8%) and 1 (0.4%), respectively. Conclusion: (1) Leiomyoma is the most common myometrial lesions and tends to coexist in a few cases with adenomyosis while majority of them show degenerative changes. (2) Menorrhagia is the most common presenting symptoms of myometrial lesions while the histologic examination is the only tool to differentiate these myometrial lesions with similar clinical symptoms. Keywords: Adenomyosis, invasive carcinosarcoma, leiomyoma, leiomyosarcoma, menorrhagi

The ASOS Surgical Risk Calculator: development and validation of a tool for identifying African surgical patients at risk of severe postoperative complications

Background: The African Surgical Outcomes Study (ASOS) showed that surgical patients in Africa have a mortality twice the global average. Existing risk assessment tools are not valid for use in this population because the pattern of risk for poor outcomes differs from high-income countries. The objective of this study was to derive and validate a simple, preoperative risk stratification tool to identify African surgical patients at risk for in-hospital postoperative mortality and severe complications. Methods: ASOS was a 7-day prospective cohort study of adult patients undergoing surgery in Africa. The ASOS Surgical Risk Calculator was constructed with a multivariable logistic regression model for the outcome of in-hospital mortality and severe postoperative complications. The following preoperative risk factors were entered into the model; age, sex, smoking status, ASA physical status, preoperative chronic comorbid conditions, indication for surgery, urgency, severity, and type of surgery. Results: The model was derived from 8799 patients from 168 African hospitals. The composite outcome of severe postoperative complications and death occurred in 423/8799 (4.8%) patients. The ASOS Surgical Risk Calculator includes the following risk factors: age, ASA physical status, indication for surgery, urgency, severity, and type of surgery. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.805 and good calibration with c-statistic corrected for optimism of 0.784. Conclusions: This simple preoperative risk calculator could be used to identify high-risk surgical patients in African hospitals and facilitate increased postoperative surveillance. © 2018 British Journal of Anaesthesia. Published by Elsevier Ltd. All rights reserved.Medical Research Council of South Africa gran