The subsequent publication of conference abstracts as a full-paper is sub-optimal in the field of neonatology. Further research is needed to identify the factors responsible for the poor subsequent publication, and efforts need to be made to address them both at the institutional and the researchers' level.

Introduction: Apparent treatment resistant hypertension (aTRH) affects 10-20% of hypertensive adults and increases risk of cardiovascular events and mortality. Fewer than half of these patients have true resistant hypertension. The majority experience pseudo-resistant hypertension due to inadequate medication adherence, white coat hypertension, and secondary causes of hypertension. We hypothesize that electronic health records can be leveraged to identify aTRH patients who would benefit from targeted counseling, medication reconciliation, and screening for secondary causes of hypertension. Methods: We studied electronic health record (EHR) data from 395 hypertensive adults in our primary care population who received longitudinal care between 2007 and 2017. Patients who met the 2008 AHA definition of resistant hypertension by chart review were considered to have aTRH. We also included 100 patients identified by heuristics targeting secondary hypertension. We extracted from the EHR demographics, vitals, laboratory results, diagnosis codes, and medications. Results outside of physiologic range were excluded and median imputation was used to handle missing data. Random forest model performance was assessed by 5-fold cross validation. Model discrimination was evaluated at an estimated positive predictive value of 75%. Results: The prevalence of aTRH in our randomly selected and full cohorts was 20.3% (n=295) and 25.8% (n=395), respectively. In cross-validation, the random forest model demonstrated a median sensitivity of 65% (IQR: 60% - 65%) and a median AUROC of 0.92 (IQR: 0.90 - 0.92). The most influential variables were related to the prescription of three or more hypertension medications; number of days on diuretics, angiotensin-converting enzyme inhibitors, or angiotensin II receptor blockers; systolic blood pressure measurements; and hypertension or diabetes diagnosis codes. Conclusion: EHR data can be used to accurately identify patients with aTRH. We expect the implementation of a clinical decision support system leveraging such models could lead to the improved care for aTRH patients.

Machine learning (ML) models trained for triggering clinical decision support (CDS) are typically either accurate or interpretable but not both. Scaling CDS to the panoply of clinical use cases while mitigating risks to patients will require many ML models be intuitively interpretable for clinicians. To this end, we adapted a symbolic regression method, coined the feature engineering automation tool (FEAT), to train concise and accurate models from high-dimensional electronic health record (EHR) data. We first present an in-depth application of FEAT to classify hypertension, hypertension with unexplained hypokalemia, and apparent treatment-resistant hypertension (aTRH) using EHR data for 1200 subjects receiving longitudinal care in a large healthcare system. FEAT models trained to predict phenotypes adjudicated by chart review had equivalent or higher discriminative performance (p < 0.001) and were at least three times smaller (p < 1 × 10−6) than other potentially interpretable models. For aTRH, FEAT generated a six-feature, highly discriminative (positive predictive value = 0.70, sensitivity = 0.62), and clinically intuitive model. To assess the generalizability of the approach, we tested FEAT on 25 benchmark clinical phenotyping tasks using the MIMIC-III critical care database. Under comparable dimensionality constraints, FEAT’s models exhibited higher area under the receiver-operating curve scores than penalized linear models across tasks (p < 6 × 10−6). In summary, FEAT can train EHR prediction models that are both intuitively interpretable and accurate, which should facilitate safe and effective scaling of ML-triggered CDS to the panoply of potential clinical use cases and healthcare practices.

Background: Transfusion of blood has become an important mode of transmission of infections such as human immunodeficiency virus and hepatitis B to the recipients. Blood transfusion is a boon in medical era if properly screened. The aim of study was to determine the seroprevalence of HIV donors in blood bank at M.Y.H. Indore.Methods: The study was conducted in the blood bank, M.Y.H. Hospital, Indore. Total 115775 donors attending blood bank were included in the study. All the donor samples were screened for detection of antibodies for human immunodeficiency virus by microwell Enzyme Linked Immunosorption Assay (ELISA) method. The seroprevalence of HIV infection among the donors was determined over a period of five years since January 2013 to December 2017.Results: Total 115775 blood donors were recorded. Out of total 115775 blood donors included in the study, replacement donor were 10766 (9.29%) while voluntary donor were 105009 (90.70%). In the duration of five-year study period, total 80 cases (0.06%) were reactive to HIV. Out of total 115775 blood donors included in the study, maximum cases i.e. 22 (0.08%) cases were found to be positive for HIV infection in year 2017. Out of 10766 replacement donors included in the study, 64 cases (0.59%) were reactive to HIV infection. While out of 105009 voluntary donors, 16 cases (0.01%) were found to be reactive to HIV infection. Voluntary donors are more as compared to the replacement donors. Number of HIV positive patients were found to more in replacement donor as compared to the voluntary donors.Conclusions: The seroprevalence of HIV is low in this study and hence it is concluded that the more the number of voluntary donors, the less the number of HIV positive cases. Voluntary donors can be motivated by proper health education and high quality screening programs.

Background: Most Indian population-based cancer registries have reported a gradual rise in the ovarian cancer incidence over the years. These neoplasms exhibit a spectrum of genetic background, much more varied than any other gynecological condition and present a big challenge to a gynecological oncologist. Therefore, proper recognition and classification of such pelvic masses is important for appropriate therapy and better prognosis. Objectives This study aimed to look at the demographics and clinical profile of various ovarian lesions in the local population of the central India. Patients and Methods: A prospective observational study was carried out on the surgically resected ovarian samples that were referred to the Pathology department over two and half year. A total of 100 ovarian cases were included. Relevant clinical information regarding age, bleeding, pain in abdomen, menstrual history, histopathological examination reports were recorded. Results: Out of 100 cases of ovarian lesions, majority were neoplastic lesions. Most of the cases of non-neoplastic ovarian lesions belonged to 31-40 years' age group, whereas most cases of neoplastic ovarian lesions belonged to 41-50 years' age group. Most common presenting symptom was abnormal uterine bleeding in non-neoplastic cases. But neoplastic cases presented mainly with abdominal pain. Conclusion: Majority of the ovarian lesions in central India population present after second parity, are benign in nature and present with abnormal uterine bleeding, whereas malignant ovarian lesions mainly present with abdominal pain and after 40 years of age.