Browsing by Subject "Tonsillectomy"

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    Cholecystectomy, gallstones, tonsillectomy, and pancreatic cancer risk: a population-based case-control study in minnesota
    (Nature Publishing Group, 2014-04-29) Zhang, J; Prizment, A E; Dhakal, I B; Anderson, K E; Department of Epidemiology, Richard M. Fairbanks School of Public Health
    Background: Associations between medical conditions and pancreatic cancer risk are controversial and are thus evaluated in a study conducted during 1994–1998 in Minnesota. Methods: Cases (n=215) were ascertained from hospitals in the metropolitan area of the Twin Cities and the Mayo Clinic. Controls (n=676) were randomly selected from the general population and frequency matched to cases by age and sex. The history of medical conditions was gathered with a questionnaire during in-person interviews. Odds ratios (OR) and 95% confidence intervals (95% CI) were estimated using unconditional logistic regression. Results: After adjustment for confounders, subjects who had cholecystectomy or gallstones experienced a significantly higher risk of pancreatic cancer than those who did not (OR (95% CI): 2.11 (1.32–3.35) for cholecystectomy and 1.97 (1.23–3.12) for gallstones), whereas opposite results were observed for tonsillectomy (0.67 (0.48–0.94)). Increased risk associated with cholecystectomy was the greatest when it occurred ⩽2 years before the cancer diagnosis (5.93 (2.36–15.7)) but remained statistically significant when that interval was ⩾20 years (2.27 (1.16–4.32)). Conclusions: Cholecystectomy, gallstones, and tonsillectomy were associated with an altered risk of pancreatic cancer. Our study suggests that cholecystectomy increased risk but reverse causality may partially account for high risk associated with recent cholecystectomy.
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    Comparison of Automated Posttonsillectomy Bleed Capture With Self-report
    (American Medical Association, 2017-08-01) Phillips, D. Ryan; Ellsperman, Susan E.; Matt, Bruce H.; Zarzaur, Ben L.; Otolaryngology -- Head and Neck Surgery, School of Medicine
    Importance: Tonsillectomy is one of the most common procedures performed by otolaryngologists and is associated with postoperative bleeding. Bleed rates are usually monitored by self-report. Objective: To evaluate whether using automated capture and reporting of pediatric posttonsillectomy bleeding is feasible and accurate compared with traditional self-reporting by the surgical team. Design, Setting, and Participants: An automated complication-reporting algorithm was designed to query the local health information exchange and then tested against self-reported tonsillectomy complication data collected from January 1, 2014, through December 31, 2015, at a tertiary pediatric hospital. The algorithm identified patients undergoing tonsillectomy and searched their postoperative encounters for a hand-selected set of diagnosis codes from the International Classification of Diseases, Ninth Revision and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision and free-text words to identify complication events. Five months of the 2014-2015 data set were used to help design the algorithm. Data from the remaining 19 months were compared with self-reported complications. Main Outcomes and Measures: Automated system findings compared with self-reported bleeding events. Results: During the 19-month period, 1017 tonsillectomies were performed. We compared the algorithm's effectiveness in finding tonsillectomy and adenotonsillectomy procedures for the evaluated surgeons with the hand-reviewed master tonsillectomy list. The algorithm reported 51 false-positive (5.01% missed) and 74 false-negative (7.28% misidentified) procedures. The algorithm agreed with self-report for 986 tonsillectomies and disagreed on 31 cases (3.05%) (κ = 0.69; 95% CI, 0.66-0.73). The algorithm was found to be sensitive to correctly identifying 60.53% (95% CI, 48.63%-71.34%) of tonsillectomies as having bleeding complications, with a specificity of 98.30% (95% CI, 97.19%-98.99%). Conclusions and Relevance: Capture of posttonsillectomy bleeding is possible through an automatic search of the medical record, although the algorithm will require continued refinement. Leveraging health information exchange data increases the possibilities of capturing complications at hospitals outside the local health system. Use of these algorithms will allow repeatable automated feedback to be provided to surgeons on a cyclical basis.
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    Quantitative Pupillometry as a Predictor of Pediatric Postoperative Opioid-Induced Respiratory Depression
    (Wolters Kluwer, 2021) Packiasabapathy, K. Senthil; Zhang, Xue; Ding, Lili; Aruldhas, Blessed W.; Pawale, Dhanashri; Sadhasivam, Senthilkumar; Anesthesia, School of Medicine
    Background: Safe postoperative pain relief with opioids is an unmet critical medical need in children. There is a lack of objective, noninvasive bedside tool to assess central nervous system (CNS) effects of intraoperative opioids. Proactive identification of children at risk for postoperative respiratory depression (RD) will help tailor analgesic therapy and significantly improve the safety of opioids in children. Quantitative pupillometry (QP) is a noninvasive, objective, and real-time tool for monitoring CNS effect-time relationship of opioids. This exploratory study aimed to determine the association of QP measures with postoperative RD, as well as to identify the best intraoperative QP measures predictive of postoperative RD in children. Methods: After approval from the institutional review board and informed parental consent, in this prospective, observational study of 220 children undergoing tonsillectomy, QP measures were collected at 5 time points: awake preoperative baseline before anesthesia induction (at the time of enrollment [T1]), immediately after anesthesia induction before morphine administration (T2), 3 minutes after intraoperative morphine administration (T3), at the end of surgery (T4), and postoperatively when awake in postanesthesia recovery unit (PACU) (T5). Intraoperative use of opioid and incidence of postoperative RD were collected. Analyses were aimed at exploring correlations of QP measures with the incidence of RD and, if found significant, to develop a predictive model for postoperative RD. Results: Perioperative QP measures of percentage pupil constriction (CONQ, P = .027), minimum pupillary diameter (MIN, P = .027), and maximum pupillary diameter (MAX, P = .034) differed significantly among children with and without postoperative RD. A predictive model including the minimum pupillary diameter 3 minutes after morphine administration (MIN3), minimum pupillary diameter normalized to baseline (MIN31), and percentage pupillary constriction after surgery (T4) standardized to baseline (T1) (CONQ41), along with the weight-based morphine dose performed the best to predict postoperative RD in children (area under the curve [AUC], 0.76). Conclusions: A model based on pre- and intraoperative pupillometry measures including CONQ, MIN, along with weight-based morphine dose-predicted postoperative RD in our cohort of children undergoing tonsillectomy. More studies with a larger sample size are required to validate this finding.