Search

Evaluation of novel code-based algorithms to identify invasive Escherichia coli disease (IED) among patients in healthcare databases.Inpatient visits with microbiological evidence of invasive bacterial disease were extracted from the Optum© electronic health record database between January 1, 2016 and June 30, 2020. Six algorithms, derived from diagnosis and drug exposure codes associated to infectious diseases and Escherichia coli, were developed to identify IED. The performance characteristics of algorithms were assessed using a reference standard derived from microbiology data.Among 97 194 eligible records, 25 310 (26.0%) were classified as IED. Algorithm 1 (diagnosis code for infectious invasive disease due to E. coli) had the highest positive predictive value (PPV; 96.0%) and lowest sensitivity (60.4%). Algorithm 2, which additionally included patients with diagnosis codes for infectious invasive disease due to an unspecified organism, had the highest sensitivity (95.5%) and lowest PPV (27.8%). Algorithm 4, which required patients with a diagnosis code for infectious invasive disease due to unspecified organism to have no diagnosis code for non-E. coli infections, achieved the most balanced performance characteristics (PPV, 93.6%; sensitivity, 78.1%; FAlgorithm 4, which achieved the most balanced performance characteristics, offers a useful tool to identify patients with IED and assess the burden of IED in healthcare databases.

Stephen P Fortin,1 William Petraiuolo,2 Guy Cafri,1 Gustavo Scapini,3 Pratyush Agarwal,4 Divya Chakke,4 Stephen Johnston,1 Barbara H Johnson,1 Paul M Coplan,1,5 Shumin Zhang1 1MedTech Epidemiology and Real-World Data Science, Office of the Chief Medical Officer, Johnson & Johnson, New Brunswick, NJ, USA; 2Medical Affairs, Ethicon, Cincinnati, OH, USA; 3Regional Medical Affairs, Johnson & Johnson, São Paulo, Brazil; 4Mu Sigma, Bengaluru, Karnataka, India; 5University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USACorrespondence: Stephen P Fortin, MedTech Epidemiology and Real-World Data Science, Office of the Chief Medical Officer, Johnson & Johnson, 410 George St, New Brunswick, NJ, USA, Tel +1 908 927 4844, Email sfortin1@its.jnj.comPurpose: Linear surgical staplers reduce rates of surgical adverse events (bleeding, leaks, infections) compared to manual sutures thereby reducing patient risks, surgeon workflow disruption, and healthcare costs. However, further improvements are needed. Ethicon Gripping Surface Technology (GST) reloads, tested and approved by regulatory authorities in combination with powered staplers, may reduce surgical risks through improved tissue grip. While manual staplers are used in some regions due to affordability, clinical data on GST reloads used with manual staplers are unavailable. This study compared surgical adverse event rates of manual staplers with GST vs standard reloads. These data may be used for label changes in China and Latin America.Patients and Methods: Patients undergoing general or thoracic surgery between October 1, 2015 and August 31, 2021 using ECHELON FLEX™ manual staplers with GST or standard reloads were identified from the Premier Healthcare Database. GST reloads were compared to standard reloads for non-inferiority in bleeding and anastomotic leak for general surgery. Secondary outcomes included sepsis for general surgery, and bleeding and prolonged air leak for thoracic surgery. Covariate balancing was performed using stable balancing weights.Results: The general and thoracic surgery cohorts contained 4571 (GST: 2780; standard: 1791) and 814 (GST: 514; standard: 300) patients, respectively. GST reloads were non-inferior to standard reloads for bleeding and anastomotic leak (adjusted cumulative incidence ratio: 1.02 [90% CI: 0.71, 1.45] and 1.03 [90% CI: 0.72, 1.46], respectively) for general surgery. Compared with standard reloads, GST reloads had a similar incidence of sepsis (2.2% vs 2.1%) for general surgery and lower incidences of bleeding (9.5% vs 16.0%) and prolonged air leak (12.6% vs 14.0%,) for thoracic surgery.Conclusion: GST reloads, compared to standard reloads, used with ECHELON FLEX™ manual staplers had comparable perioperative bleeding and anastomotic leak for general surgery, and lower incidences of safety events for thoracic surgery.Keywords: real-world evidence, safety, ECHELON, general surgery, thoracic surgery

Propensity score matching (PSM) is subject to limitations associated with limited degrees of freedom and covariate overlap. Cardinality matching (CM), an optimization algorithm, overcomes these limitations by matching directly on the marginal distribution of covariates. This study compared the performance of PSM and CM.Comparative cohort study of new users of angiotensin-converting enzyme inhibitor (ACEI) and β-blocker monotherapy identified from a large U.S. administrative claims database. One-to-one matching was conducted through PSM using nearest-neighbor matching (caliper = 0.15) and CM permitting a maximum standardized mean difference (SMD) of 0, 0.01, 0.05, and 0.10 between comparison groups. Matching covariates included 37 patient demographic and clinical characteristics. Observed covariates included patient demographics, and all observed prior conditions, drug exposures, and procedures. Residual confounding was assessed based on the expected absolute systematic error of negative control outcome experiments. PSM and CM were compared in terms of post-match patient retention, matching and observed covariate balance, and residual confounding within a 10%, 1%, 0.25% and 0.125% sample group.The eligible study population included 182 235 (ACEI: 129363; β-blocker: 56872) patients. CM achieved superior patient retention and matching covariate balance in all analyses. After PSM, 1.6% and 28.2% of matching covariates were imbalanced in the 10% and 0.125% sample groups, respectively. No significant difference in observed covariate balance was observed between matching techniques. CM permitting a maximum SMD0.05 was associated with improved residual bias as compared to PSM.We recommend CM with more stringent balance criteria as an alternative to PSM when matching on a set of clinically relevant covariates.

In real-world evidence research, reliability of coding in healthcare databases dictates the accuracy of code-based algorithms in identifying conditions such as urinary tract infection (UTI). This study evaluates the performance characteristics of code-based algorithms to identify UTI.Retrospective observational study of adults contained within three large U.S. administrative claims databases on or after January 1, 2010. A targeted literature review was performed to inform the development of 10 code-based algorithms to identify UTIs consisting of combinations of diagnosis codes, antibiotic exposure for the treatment of UTIs, and/or ordering of a urinalysis or urine culture. For each database, a probabilistic gold standard was developed using PheValuator. The performance characteristics of each code-based algorithm were assessed compared with the probabilistic gold standard.A total of 2 950 641, 1 831 405, and 2 294 929 patients meeting study criteria were identified in each database. Overall, the code-based algorithm requiring a primary UTI diagnosis code achieved the highest positive predictive values (PPV;93.8%) but the lowest sensitivities (12.9%). Algorithms requiring three UTI diagnosis codes achieved similar PPV (0.899%) and improved sensitivity (41.6%). Algorithms requiring a single UTI diagnosis code in any position achieved the highest sensitivities (72.1%) alongside a slight reduction in PPVs (78.3%). All-time prevalence estimates of UTI ranged from 21.6% to 48.6%.Based on these findings, we recommend use of algorithms requiring a single UTI diagnosis code, which achieved high sensitivity and PPV. In studies where PPV is critical, we recommend code-based algorithms requiring three UTI diagnosis codes rather than a single primary UTI diagnosis code.

Kristin Kostka,1,2 Talita Duarte-Salles,3 Albert Prats-Uribe,4 Anthony G Sena,5,6 Andrea Pistillo,3 Sara Khalid,4 Lana YH Lai,7 Asieh Golozar,8,9 Thamir M Alshammari,10 Dalia M Dawoud,11 Fredrik Nyberg,12 Adam B Wilcox,13,14 Alan Andryc,5 Andrew Williams,15 Anna Ostropolets,16 Carlos Areia,17 Chi Young Jung,18 Christopher A Harle,19 Christian G Reich,1,2 Clair Blacketer,5,6 Daniel R Morales,20 David A Dorr,21 Edward Burn,3,4 Elena Roel,3,22 Eng Hooi Tan,4 Evan Minty,23 Frank DeFalco,5 Gabriel de Maeztu,24 Gigi Lipori,19 Hiba Alghoul,25 Hong Zhu,26 Jason A Thomas,13 Jiang Bian,19 Jimyung Park,27 Jordi Martínez Roldán,28 Jose D Posada,29 Juan M Banda,30 Juan P Horcajada,31 Julianna Kohler,32 Karishma Shah,33 Karthik Natarajan,16,34 Kristine E Lynch,35,36 Li Liu,37 Lisa M Schilling,38 Martina Recalde,3,22 Matthew Spotnitz,14 Mengchun Gong,39 Michael E Matheny,40,41 Neus Valveny,42 Nicole G Weiskopf,21 Nigam Shah,29 Osaid Alser,43 Paula Casajust,42 Rae Woong Park,27,44 Robert Schuff,21 Sarah Seager,1 Scott L DuVall,35,36 Seng Chan You,45 Seokyoung Song,46 Sergio Fernández-Bertolín,3 Stephen Fortin,5 Tanja Magoc,19 Thomas Falconer,16 Vignesh Subbian,47 Vojtech Huser,48 Waheed-Ul-Rahman Ahmed,33,49 William Carter,38 Yin Guan,50 Yankuic Galvan,19 Xing He,19 Peter R Rijnbeek,6 George Hripcsak,16,34 Patrick B Ryan,5,16 Marc A Suchard,51 Daniel Prieto-Alhambra4 1IQVIA, Cambridge, MA, USA; 2OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA; 3Fundació Institut Universitari per a la recerca a l’Atenció Primà ria de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; 4Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK; 5Janssen Research & Development, Titusville, NJ, USA; 6Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands; 7School of Medical Sciences, University of Manchester, Manchester, UK; 8Regeneron Pharmaceuticals, Tarrytown, NY, USA; 9Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 10College of Pharmacy, Riyadh Elm University, Riyadh, Saudi Arabia; 11National Institute for Health and Care Excellence, London, UK; 12School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 13Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA; 14Unviersity of Washington Medicine, Seattle, WA, USA; 15Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; 16Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA; 17Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; 18Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, South Korea; 19University of Florida Health, Gainesville, FL, USA; 20Division of Population Health and Genomics, University of Dundee, Dundee, UK; 21Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; 22Universitat Autònoma de Barcelona, Barcelona, Spain; 23O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, Canada; 24IOMED, Barcelona, Spain; 25Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine; 26Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 27Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea; 28Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain; 29Department of Medicine, School of Medicine, Stanford University, Redwood City, CA, USA; 30Georgia State University, Department of Computer Science, Atlanta, GA, USA; 31Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d’Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona, Universitat Pompeu Fabra, Barcelona, Spain; 32United States Agency for International Development, Washington, DC, USA; 33Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK; 34New York-Presbyterian Hospital, New York, NY, USA; 35VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; 36Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; 37Biomedical Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 38Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; 39Institute of Health Management, Southern Medical University, Guangzhou, People’s Republic of China; 40Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA; 41Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; 42Real-World Evidence, TFS, Barcelona, Spain; 43Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 44Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; 45Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea; 46Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Daegu, South Korea; 47College of Engineering, The University of Arizona, Tucson, AZ, USA; 48National Library of Medicine, National Institutes of Health, Bethesda, MD, USA; 49College of Medicine and Health, University of Exeter, St Luke’s Campus, Exeter, UK; 50DHC Technologies Co. Ltd., Beijing, People’s Republic of China; 51Departments of Biostatistics, Computational Medicine, and Human Genetics, University of California, Los Angeles, CA, USACorrespondence: Daniel Prieto-Alhambra, Botnar Research Centre, Windmill Road, Oxford, OX37LD, UK, Email daniel.prietoalhambra@ndorms.ox.ac.ukPurpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.Keywords: OHDSI, OMOP CDM, descriptive epidemiology, real world data, real world evidence, open science

Manual circular staplers are widely used in colorectal surgery; however, limited literature exists examining complications related to circular anastomoses when such devices are used. The present study evaluated the incidence, predictors, and economic burden of circular anastomotic complications in left-sided colorectal reconstructions involving manual circular staplers. Patients aged ≥18 years who underwent hemicolectomy, low anterior resection, or sigmoidectomy between 1 October 2016 and 31 December 2018 were identified from the Premier Healthcare Database. Manual circular stapler use was identified from hospital administrative billing records. Circular anastomotic complications were defined as a composite endpoint of multiple circular stapler use (proxy for stapler failure) or other circular anastomotic complications (anastomotic leak, bleeding, device/surgical complications, infection, and transfusion). Multivariable analyses were used to model the associations between circular anastomotic complications and total hospital costs, length of stay, operating room time, and 30-, 60-, and 90-day readmission rates. A total of 13,167 patients met the study criteria, of whom 2,984 (22.7%) had circular anastomotic complications. Predictors of circular anastomotic complications included age, procedure type, provider region, and select patient comorbidities. As compared with those who did not, patients who suffered circular anastomotic complications had significantly higher adjusted total hospital costs ($26,924 vs. $18,748; p p p p p p p The present study is limited by the observational nature and potential for measurement error that is inherent to administrative healthcare databases. In this analysis of patients undergoing left-sided colorectal reconstructions involving a manual circular stapler, circular anastomotic complications were associated with adverse economic consequences.

Background Context Spinal fusion surgeries are one of the most common types of operations performed during inpatient stays in the United States. Successful wound closure, including watertight closure at the skin layer, plays in important role in patient outcomes. Purpose To compare the economic and clinical outcomes of spinal fusion surgeries using one of two sutureless skin closure techniques: skin staples plus waterproof wound dressings (SSWWD) or 2-octyl cyanoacrylate plus polymer mesh tape (2OPMT). Study Design/Setting Retrospective study using a multi-hospital database Patient Sample Patients undergoing inpatient spinal fusion surgery for a spine disorder between October 1, 2015 and March 31, 2019. Outcome Measures Total costs from the hospital perspective, operating room time (ORT), hospital length of stay (LOS), non-home discharge, infection/wound complications during the 90-day global period (index surgery through 90 days post-discharge), and 30/60/90-day all-cause readmissions. Methods Outcomes were compared between study groups using nearest neighbor propensity score matching with exact matching on 45 primary procedure/diagnosis code groupings and generalized estimating equations to account for hospital-level clustering. This study was sponsored by Ethicon, Inc., a Johnson & Johnson company; the authors are employees or consultants of Johnson & Johnson. Results A total of 11,991 patients met the study criteria (2OPMT=5,961; SSWWD=6,030), of which 3,602 were included in each post-match study comparison group (total=7,204). As compared with the SSWWD group, the 2OPMT group had statistically significant lower median ORT (240 vs. 270 minutes; p=0.002), mean LOS (3.35 [SD=2.6] vs. 3.86 [SD=2.8] days, p=0.031), risks of non-home discharge status (17.63% vs. 23.10%, p=0.035), overall infections/wound complications (1.37% vs. 2.48%, p=0.015), and surgical site infection (1.11% vs. 2.07%, p=0.023). Differences between the study groups in total hospital costs, all-cause readmissions, and other sub-components of the infection/wound complication composite outcome were statistically insignificant (p>0.05). Conclusions In this retrospective observational study of patients undergoing elective inpatient spinal fusion surgery, the use of 2OPMT for skin closure was associated with significantly lower ORT, LOS, non-home discharge, and 90-day rates of infections/wound complications as compared with SSWWD.