Appendix a Common Abbreviations Used in Medication
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UNIVERSITY OF AMSTERDAM MASTERS THESIS Impact of Medication Grouping on Fall Risk Prediction in Elders: A Retrospective Analysis of MIMIC-III Critical Care Database Student: SRP Mentor: Noman Dormosh Dr. Martijn C. Schut Student No. 11412682 – SRP Tutor: Prof. dr. Ameen Abu-Hanna SRP Address: Amsterdam University Medical Center - Location AMC Department Medical Informatics Meibergdreef 9, 1105 AZ Amsterdam Practice teaching period: November 2018 - June 2019 A thesis submitted in fulfillment of the requirements for the degree of Master of Medical Informatics iii Abstract Background: Falls are the leading cause of injury in elderly patients. Risk factors for falls in- cluding among others history of falls, old age, and female gender. Research studies have also linked certain medications with an increased risk of fall in what is called fall-risk-increasing drugs (FRIDs), such as psychotropics and cardiovascular drugs. However, there is a lack of consistency in the definitions of FRIDs between the studies and many studies did not use any systematic classification for medications. Objective: The aim of this study was to investigate the effect of grouping medications at different levels of granularity of a medication classification system on the performance of fall risk prediction models. Methods: This is a retrospective analysis of the MIMIC-III cohort database. We created seven prediction models including demographic, comorbidity and medication variables. Medica- tions were grouped using the anatomical therapeutic chemical classification system (ATC) starting from the most specific scope of medications and moving up to the more generic groups: one model used individual medications (ATC level 5), four models used medication grouping at levels one, two, three and four of the ATC and one model did not include med- ications. We compared the predictive performance of these models with the performance of a model with a predefined ATC groups of FRIDs based on the literature and expert review (Expert-Opinion). Medications and comorbidities were extracted from the discharge sum- maries. Logistic regression with least absolute shrinkage and selection operator (LASSO) was used to construct the prediction models. The main performance measure was the area under the receiver operating characteristic curve (AUC-ROC). Additionally, we systemati- cally evaluated the performance by including area under the precision-recall curve (AUC- PR), Akaike Information Criterion (AIC), sensitivity, specificity, precision and the Brier score. Calibration was assessed using calibration plots. In order to internally validate the results, we performed bootstrapping to obtain optimism-corrected estimates and Wilcoxon signed- rank test to test the significance of the median differences of estimates between all the models and the expert-opinion FRIDs model. Results: The highest statistically significant AUC-ROC was achieved by grouping medi- cations at level two of the ATC [0.7143 (CI 95%: 0.710-0.718) compared to 0.698 (CI 95%: 0.6927-0.7018) of the Expert-Opinion model]. The AUC-PR of all the models with medica- tions significantly outperformed the Expert-Opinion model except the ATC-1 model. The values of the other performance measures were varied between the models depending on the grouping level. All the models showed satisfactory calibration. Conclusion: The performance of fall risk prediction model is significantly affected by medi- cation grouping. The discrimination performance is enhanced by grouping the medications using the ATC classification system instead of using a FRIDs list. The optimal grouping level can be determined through experimentation. v Samenvatting Achtergrond: Vallen bij ouderen is een belangrijkste oorzaak van letsel. Risicofactoren voor vallen, waaronder onder meer zijn geschiedenis van vallen, ouderdom en vrouwelijk ges- lacht. Onderzoeksstudies hebben ook bepaalde medicijnen in verband gebracht met een verhoogd valrisico bij zogenaamde valrisicoverhogende geneesmiddelen (FRIDs), zoals psy- chotropen en cardiovasculaire geneesmiddelen. Er is echter een gebrek aan consistentie in de definities van FRIDs tussen de studies en veel studies hebben geen systematische classi- ficatie voor medicijnen gebruikt. Doel: Het doel van deze studie was om het effect te onderzoeken van het groeperen van medicijnen op verschillende niveaus van granulariteit van een medicatieclassificatiesysteem op de prestaties van val-risicovoorspellingsmodellen Methoden: Dit is een retrospectieve analyse van de MIMIC-III cohortdatabase. We hebben zeven voorspellingsmodellen gemaakt, waaronder demografische, comorbiditeits- en med- icatievariabelen. Medicijnen werden gegroepeerd met behulp van het anatomische thera- peutische chemische classificatiesysteem (ATC) uitgaande van de meest specifieke reikwi- jdte van medicijnen en oplopend naar de meer generieke groepen: één model gebruikte individuele medicijnen (ATC niveau 5), vier modellen gebruikten medicatiegroepering op niveau één, twee, drie en vier van de ATC en één model zonder medicijnen. We hebben de voorspellende prestaties van deze modellen vergeleken met de prestaties van een model met vooraf gedefinieerde ATC groepen van FRIDs op basis van de literatuur en expert review (Expert-Opinion). Medicijnen en comorbiditeiten werden uit ontslagsamenvattin- gen gehaald. Logistieke regressie met minst absolute krimp en selectie-operator (LASSO) werd gebruikt om de voorspellingsmodellen te construeren. De belangrijkste prestatiemaat- staf was “area under the receiver operating characteristic curve” (AUC-ROC). Bovendien hebben we de prestaties systematisch geëvalueerd door Akaike Information Criterion (AIC) op te nemen, area under precision recall (AUC-PR), sensitiviteit, specificiteit, precisie en de Brier-score. Om de resultaten intern te valideren, hebben we bootstrapping uitgevoerd om optimisme corrigeerde schattingen te verkrijgen en Wilcoxon signed-rank test om de signif- icantie van de mediaan te testen op verschillen in schattingen tussen alle modellen en het (Expert-Opinion) model. Resultaten: De hoogste statistisch significante AUC-ROC werd bereikt door medicatie te groeperen op niveau twee van de ATC [0.7143 (CI 95 %: 0.710-0.718) vergeleken met 0.698 (CI 95 %: 0.6927-0.7018) van het Expert-Opinion model]. De AUC-PR van alle modellen met medicijnen overtrof aanzienlijk het Expert-Opinion-model behalve het ATC-1 model. De waarden van de andere prestatiemetingen varieerden tussen de modellen, afhankelijk van het groeperingsniveau. Alle modellen vertoonden een bevredigende kalibratie. Conclusie: De prestaties van val-risicovoorspelling worden aanzienlijk beïnvloed door med- icatiegroepering. De discriminatieprestaties van de val voorspellen is verbeterd door de hele medicatie te groeperen met behulp van het ATC classificatiesysteem in plaats van de FRIDs lijst. Het optimale groeperingsniveau kan worden bepaald door middel van experimenten. vii Acknowledgements I would like first of all to thank my wife Heba. She has been extremely support- ive of me throughout my life and has made innumerable sacrifices to help me get to this point. Thanks to my children, Boshra, Mohammad, and the triplets Omar, Huda and Hamza, who have continually provided the requisite motivation to finish my degree. My parents who taught me to pursue perfection instead of excellence, deserve special thanks. Without such a team behind me, I doubt that I would be in this place today. I would like to thank my tutor Prof. Dr. Ameen Abu Hanna and the daily mentor, Dr. Martijn Schut, for their prompt guidance and support through the SRP-process. I am also grateful to Prof. Dr. Nathalie van der Velde who provided valuable inputs and feedback to the SRP. Special thanks go to Dr. Ronald Cornet who granted me the permission to access the data used in this thesis, the MIMIC database. Last but not least, thanks to you reader. You have read at least one page of my thesis. Thank You.. ix Contents Abstract iii Abstract (Dutch) v Acknowledgements vii 1 Introduction 1 1.1 Research Questions . .2 1.2 Outline of this Thesis . .3 2 Background 5 2.1 Fall-Risk-Increasing Drugs (FRIDs) . .5 2.2 Medical Information Mart for Intensive Care III (MIMIC-III) database .6 2.3 Anatomical Therapeutic Chemical Classification System (ATC) . .7 2.4 Least Absolute Shrinkage and Selection Operator (LASSO) . .8 3 Materials and Methods 11 3.1 Study Design . 11 3.2 Population . 11 3.3 Patient and Event Inclusion . 12 3.4 Data Extraction . 13 3.5 Data Processing . 14 3.6 Clinical Outcome . 15 3.7 Outcome Measurements: Predictive Performance . 15 3.8 Statistical Analysis . 15 3.9 Models Validation . 16 4 Results 17 4.1 Study Population . 17 4.2 Extracted Variables . 18 4.3 Missing Variables . 19 4.4 Measures of Predictive Performance . 19 5 Discussion and Future Direction 25 Appendix 29 x A Common Abbreviations Used in Medication Extraction 31 B Average Predictive Comparison (APC) 33 Bibliography 49 1 Chapter 1 Introduction About one-third of community-dwelling elderly people aged 65 years and more fall at least once per year [1]. Falls are the major cause of injury among elders and result in high healthcare demand. Age-related problems, including falls, are expected to increase in the coming decades due to aging population. As a consequence, falls are expected to become one of the major public health problems worldwide [2–4]. There are several risk factors for falls including among others history of falls, old age, fe- male gender and mobility problems [5–8]. Research studies have also linked certain