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HEDEA: A Python Tool for Extracting and Analysing Semi-structured Information from Medical Records
Anshul Aggarwal,Sunita Garhwal,Ajay Kumar 대한의료정보학회 2018 Healthcare Informatics Research Vol.24 No.2
Objectives: One of the most important functions for a medical practitioner while treating a patient is to study the patient’s complete medical history by going through all records, from test results to doctor’s notes. With the increasing use of technology in medicine, these records are mostly digital, alleviating the problem of looking through a stack of papers, which are easily misplaced, but some of these are in an unstructured form. Large parts of clinical reports are in written text form and are tedious to use directly without appropriate pre-processing. In medical research, such health records may be a good, convenient source of medical data; however, lack of structure means that the data is unfit for statistical evaluation. In this paper, we introduce a system to extract, store, retrieve, and analyse information from health records, with a focus on the Indian healthcare scene. Methods: A Python-based tool, Healthcare Data Extraction and Analysis (HEDEA), has been designed to extract structured information from various medical records using a regular expression-based approach. Results: The HEDEA system is working, covering a large set of formats, to extract and analyse health information. Conclusions: This tool can be used to generate analysis report and charts using the central database. This information is only provided after prior approval has been received from the patient for medical research purposes.
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Manoj Kumar,Siddharth Pandey,Ajay Aggarwal,Deepanshu Sharma,Gaurav Garg,Samarth Agarwal,Ashish Sharma,Satyanarayan Sankhwar 대한비뇨의학회 2018 Investigative and Clinical Urology Vol.59 No.5
Purpose: To see the 30-day unplanned readmission rates in patients underdoing endo-urological surgeries for upper urinary tract calculi we conducted this retrospective study at King George's Medical University, Lucknow, India. Unplanned readmissions not only add to healthcare costs but also are bothersome for the patients. There are many studies on 30-day unplanned readmissions in general surgical patients. Although similar studies have been done in certain urological procedures, no study has reported readmission rates or its risk factors in patients undergoing surgeries for upper urinary tract calculi. Materials and Methods: We retrospectively reviewed our prospectively maintained database from 1st January 2009 to 31st December 2017, for the patients who underwent endo-urological procedures for upper urinary tract calculi and identified the patients who were re-admitted within 30 days of discharge. Results: Out of the total 3,209 patients undergoing endo-urological procedures for upper urinary tract calculi 56 were re-admitted. The readmission rate was 1.74% over the study period. The most common etiology for readmission was sepsis followed by hematuria. The significant risk factors for readmission in bivariate analysis included male gender, age >65 years, current smoking, chronic obstructive pulmonary disease, diabetes mellitus, bleeding disorder, prior cardiac disease, and American Society of Anesthesiologists (ASA) class ≥3. In multivariate risk adjusted logistic regression analysis ASA class ≥3 was the only independent risk factor for readmission. Conclusions: The readmission rates in endo-urological procedures for urolithiasis are less compared to other procedures. ASA class ≥3 is the most important independent predictor of unplanned 30-day readmissions.
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Siddharth Pandey,Deepanshu Sharma,Satyanarayan Sankhwar,Manmeet Singh,Gaurav Garg,Ajay Aggarwal,Ashish Sharma,Samarth Agarwal 대한비뇨의학회 2018 Investigative and Clinical Urology Vol.59 No.6
Purpose: To compare patients with sepsis due to obstructive urolithiasis (Sep-OU) and underwent drainage by percutaneous nephrostomy (PCN) or a double-J (DJ)-ureteral stent and to identify predictive risk factors of DJ stent failure in these patients. Materials and Methods: We reviewed our records from January 2013 to July 2018 and identified 286 adult patients with Sep-OU out of which 36 had bilateral involvement, thus total 322 renal units were studied. Urologic residents in training carried out both ureteral stenting and PCN tube placement. Demographic data and stone characteristics were recorded along with Charlson comorbidity index. For predicting risk factors of DJ stent failure, those variables that had a p-value <0.1 in univariate analysis were combined in a multinomial regression analysis model. Results: The patients with PCN placement were significantly older than those with DJ stent placement (p=0.001) and also had significant number of units with multiple calculi (p=0.018). PCN was also placed more frequently in those patients with a upper ureteric calculi (p<0.05). On multinomial regression analysis multiple calculi (p=0.014; odds ratio [OR], 4.878; 95% confidence interval [CI], 1.377–17.276) and larger calculi size (p=0.040; OR, 0.974; 95% CI, 0.950–0.999) were the significant predictors of DJ stent failure. Conclusions: In patients with sepsis from obstructive urolithiasis due to larger and multiple calculi a PCN placement might be better suited although this data requires further prospective randomized studies to be extrapolated.
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Siddharth Pandey,Satyanarayan Sankhwar,Apul Goel,Manoj Kumar,Ajay Aggarwal,Deepanshu Sharma,Samarth Agarwal,Tushar Pandey 대한비뇨의학회 2019 Investigative and Clinical Urology Vol.60 No.2
Purpose: To analyze the utility of quick Sequential Organ Failure Assessment (qSOFA) in patients with uro-sepsis due to acute pyelonephritis (APN) with upper urinary tract calculi, we conducted this study. The role of qSOFA as a tool for rapid prognostication in patients with sepsis is emerging. But there has been a great debate on its utility. Literature regarding utility of qSOFA in uro-sepsis is scarce. Materials and Methods: Ours was a retrospective study including 162 consecutive patients who were admitted for APN with upper urinary tract calculi over a 3 and half years (total 42 months) period. We evaluated the accuracy of qSOFA in predicting inhospital mortality and intensive care unit (ICU) admissions and compared this with the predictive accuracy of systemic inflammatory response syndrome (SIRS). We used the Area Under Curve (AUC) of the Receiver Operator Characteristic curve to calculate it and also calculated the optimum cut off for qSOFA score. Results: The overall mortality and ICU admission rates were 7.4% and 12.9%, respectively. qSOFA had a higher predictive accuracy for in-hospital mortality (AUC, 0.981; 95% confidence interval [CI], 0.962–1.000) and ICU admissions (AUC, 0.977; 95% CI, 0.955–0.999) than SIRS. A qSOFA score of ≥2 was an optimum cut off for predicting prognosis. In a multivariate model qSOFA ≥2 was a highly significant predictor of in-hospital mortality and ICU admissions (p<0.001). Conclusions: qSOFA is a reliable and rapid bedside tool in patients with sepsis with accuracy more than SIRS in predicting inhospital mortality and ICU admissions.
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