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1645 ainsworth

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1645 ainsworth

  1. 1. Predicting Patient Risk of Patient Acquisition of Klebsiella pneumoniae carbapenemase producing Organisms (KCPO) with Relation to the Hospital Environment Dr. Amy Mathers, Infectious Disease John Ainsworth, Senior Data Scientist
  2. 2. The Problem • From 2007 to 2015 our hospital sustained a low frequency outbreak of Klebsiella pneumoniae carbapenemase producing Organisms (KCPO) • Dr. Amy Mathers had discovered a common genetic signature in organisms colonizing and infecting patients, as well as in the hospital environment • Dr. Mathers asked the Data Science team for assistance in organizing her data, visualizing results, uncovering patient risk, and linking environmental exposure to patient acquisition of KPCO Colonized Infected AdjustedRate
  3. 3. Overview • Part I: Developing a Patient Risk Model • Part II: Treatment Effects Model for Environmental Exposure
  4. 4. 2004 Patient Care derived from Billing Data 2016 2010 Patient Care Derived from Epic EMR Patient Sampling 2007 Genomic Sequence Data In progress 2009 Theradoc Data Sources for Infectious Disease Modeling 2012 Environmental Sampling 2013 Build Risk Model on 2007-2012 data Evaluate Model and examine treatment effect of Room Exposure on 2013-2015
  5. 5. PART I: PATIENT RISK MODEL
  6. 6. Case Control Approach • We wanted to enrich for patients who would be most likely to acquire Klebsiella pneumoniae carbapenemase-producing Enterobacteriaceae/Aeromonas (KPCEA) • Our KPCEA peri-rectal screening protocol screens all patients on admission and then weekly for the long term acute care hospital, Surgical Intensive Care Unit, Medical Intensive Care Unit and then weekly for all the patients on a unit where another patient is colonized • 2 or more Negative Klebsiella pneumoniae carbapenemase-producing Enterobacteriaceae/Aeromonas (KPCEA) screenings • This enriches the control patients in similar high risk areas who have a longer length of stay (>7 days as KPCEA screening are usually more than a week apart) – Positive cases screened for genetic match & removal of imports and patients where transmission can be explained by patient to patient overlap on the same unit at the same time with a genetically linked isolate (same species when genomics not available)
  7. 7. Removed patients with a transmission chain linked to another patient • Also wanted to target patients without an alternative traditional explanation for KPCEA acquisition to examine the risk factors most likely to be associated with possible environmental acquisition • Excluded Imports from other hospitals- From the case definition excluded Import (patient where KPCE identified before admission at another hospital or went positive <48 hours after admission) • Excluded Patient Overlap Cases- Positive cases examined for a genetic match (species matched when genomics not available) with another patient when both patients were simultaneously on the same ward before a patient acquires KPCEA
  8. 8. Data Prep for Patient Risk • 20 factors identified by interview with Dr. Amy Mathers for observed patient commonalities • Data Sources & Methods – Accounting system data • CPT codes • HCPCS codes • ICD-9 Diagnosis & Procedure Codes – Centricity Data (Surgical Data) • CPT Codes – CPT codes were rolled up into higher level categories using UMLS CPT categories – ICD-9 Codes were rolled up into higher level categories using CCS Data – Anti-Infective Medications were identified from HCPCS code and HCPCS to medication NDC cross-walk – Generated a T/F flag for all factors occurring over a 90 day lookback period • Table 1 created to validate patient population match between positive & negative cases in appendix • Positive cases are chosen at point 2 days prior to identification of infection • Negative cases are chosen by randomly sampling a point in time of their stay in order to contrast negative patients at variable points in their stay with the positive cases at a specific point in time
  9. 9. Table 1 Case Control Study Gender Age Range Length Of Stay Anti-Infective Charlson Charlson Pos Count Neg Count 0 19 320 1 10 192 2 19 243 3 14 190 4 14 168 5 6 107 6 11 133 7 16 86 8 10 75 9 4 37 10 3 18 11 4 13 12 1 4 13 1 1 14 0 1 Total 132 1588 LOS Pos Days Neg Days 0-7 12 143 8-14 13 429 15-30 38 602 31-90 58 350 91-365 11 57 >365 0 7 Total 132 1588 Anti-Infective Pos Count Neg Count F 4 163 T 128 1425 Total 132 1588 Gender Pos Count Neg Count Female 55 672 Male 77 916 Total 132 1588 Age Pos Count Neg Count 18-44 25 287 45-64 58 702 65+ 49 599 Total 132 1588
  10. 10. Model Info • Training Set – 132 KPC positive cases • 30 Nov 2007 – 2 Oct 2012 – 1588 negative cases validated by 2 negative CRE screenings • 24 Dec 2009 – 24 Dec 2012 • Validation Set – 1 Jan 2013 – 24 Feb 2015 • Out of Time validation – 76 KPC positive cases – 46870 negative or unknown cases • Built a Naïve Bayesian Model – The class probability is the product of the probability per attribute and the probability of the class attribute itself. – The probability for nominal values is the number of occurrences of the class value with the given value divided by the number of total occurrences of the class value. Variable Strength and Direction Difference from probability of class attribute Presence of factors indicates an increased risk of acquisition Absence of factors indicates a decreased risk of acquisition
  11. 11. Validation Model Performance • Training AUC 0.746 • Threshold analysis suggest cutoff of 0.09 F1 Thresh : 0.5 F3 Thresh : 0.09 F2 Thresh : 0.28
  12. 12. Example of at Risk Patients in Hospital on a Given Date in 2016 Unique Patients Given Anonymized Identifiers (A-J) Clinical Factors A B C D E F G H I J AbdominalSurgeryIntroduction 0 0 1 1 0 1 0 0 0 0 AbdominalSurgeryEndoscope 1 1 1 0 1 1 1 1 1 1 RepiratorySurgeryIntroduction 1 0 1 0 1 0 1 1 1 1 RepiratorySurgeryEndoscope 1 0 1 1 0 1 1 1 0 0 RepiratorySurgeryIncision 1 1 0 1 0 1 0 0 1 1 SurgicalProceduresSkin 0 1 0 1 0 0 0 0 0 0 SurgicalRepairSkin 1 1 0 1 0 0 0 0 0 0 Wound 0 1 0 0 0 0 0 0 0 0 Mobility 1 1 1 1 1 1 1 1 1 1 MechVentilation 1 1 1 1 1 1 1 1 1 1 OtherVentilation 0 0 0 0 0 0 0 1 0 0 Nutrition 0 0 0 0 0 0 0 0 0 0 CardiovascularAngiolasty 0 0 0 0 0 0 0 0 0 0 CardiovascularIntroduction 1 0 0 0 1 0 0 0 0 0 CardiovascularVenousArterial 1 1 1 1 1 1 1 1 1 1 CardiovascularTranscatheter 0 0 0 0 1 0 0 0 0 0 BladderIntroduction 0 0 1 0 0 0 1 0 1 1 LiverTransplant 0 0 0 0 1 0 0 0 0 0 PancreasTransplant 0 0 0 0 0 0 0 0 0 0 RenalTransplant 0 0 0 0 0 0 0 0 0 0 AntiInfective 1 1 1 1 1 1 1 1 1 1 Infection 0 1 0 0 0 0 0 0 0 0 Score 91.53% 87.01% 77.99% 65.48% 62.80% 60.17% 52.37% 49.38% 44.19% 44.19% Rank 1 2 3 4 5 6 7 8 9 9
  13. 13. PART II: TREATMENT EFFECT MODEL FOR ROOM EXPOSURE
  14. 14. Analysis of impact of positive environment on patient infection • A room is defined as positive or negative for the time range between two positive (or negative) samples of any location in the room – All rooms are considered to start as negative • Patients are considered exposed to positive if at any point in their stay (prior to a positive culture) they were in a positive room • Patients are considered unexposed if they can be confirmed for all rooms of their stay to have been in a negative room • Time range is from 1 Jan 2013 to 24 Feb 2015
  15. 15. 15 Rooms are irregularly sampled and inconsistently positive
  16. 16. Raw Environment Results Outcome Status Exposure Status yes no yes 14 2821 no 2 11783 Patient Risk Patient Risk is not controlled for in this analysis
  17. 17. Treatment Effects Model • Patient Risk Value is the max value for an encounter prior to exposure • Days Prior to Exposure is patient days prior to first room exposure • All imports and patient overlap cases have been removed by rule – Imports- Isolates were classified as “imported” for patients without ANY prior inpatient admission to UVaHS Medical Center/LTACH (UVaMC) (outpatient encounters do not count) and with a CRE culture before or within 48 h of transfer to UVaMC. – Patient overlap linked- The risk of acquiring CRE at UVaMC was based on any hospitalization at UVaMC of >48 hours and classified as “patient overlap" if there was a patient infected or colonized with KPC-producing Enterobacteriaceae (KPCin the same unit simultaneously prior to isolation of a new CRE of the same species • We use the logistic regression model to control for patient risk and length of stay • The p-value of room is positive shows that changes in the ‘RoomIsPositive’ variable are significantly associated with changes in the response model (acquisition of infection) • The ‘RoomIsPositive’ coefficient converts to an Odds Ratio of 22.25 vs. 29.23
  18. 18. Final Results • Won a contract with the CDC to continue the work • Ultimately, the installation of hopper covers and Biorec p- trap heaters reduced environmental transmission
  19. 19. APPENDIX
  20. 20. CPT Code Category Rollup
  21. 21. Abdominal Surgery • Surgical Procedures on the Digestive System – Endoscopy Procedures • DIAGNOSTIC COLONOSCOPY • DIAGNOSTIC SIGMOIDOSCOPY • ENDO BILIARY W/DIL BILI DUCT • No examples in positive results of – Esophagoscopy – Proctosigmoidoscopy – Introduction (Revision, Removal) Procedures • INTRO, GASTROINTESTINAL TUBE • PLACE TUBE NASOGASTRIC • CHANGE G-TUBE TO G-J PERC • EXCHG CATH ABSC/CYST DRAIN
  22. 22. Respiratory Surgery • Surgical Procedures on the Respiratory System (Larynex, Lungs, Trachea) – Endoscopy Procedures • Bronchoscopy – DX BRONCHOSCOPE/WASH – Introduction Procedures • INTUBATI AIRWAY – Incision Procedures • Tracheotomy – INCISION OF WINDPIPE – THORACOSTOMY INS CHEST TUBE
  23. 23. Burns • Anesthesia for 2nd and 3rd degree burns • Burn dressing/debridement – DRESS/DEB BURN
  24. 24. Mobility • Physical Medicine and Rehabilitation – Therapeutic Procedures • THERAPEUTIC ACTIVITY 15 MIN • GAIT TRAINING 15 MIN • Orthotic Management – ORTHOTIC MNGMNT/FIT/TRAN 15 • Fractures? – Casts – Splints – Removal of Casts & Splints
  25. 25. Cardiovascular Procedures • Arteries – Transluminal Angioplasty Procedures • ANGIOPL TRNLM RENAL VISC PERC – Intravenous Vascular Introduction • CATHETER VENOUS 2ND ORDER • IV INFUSION START – Venous Procedures • VENIPUNCTURE – Central Venous Access Procedures • INS CATH CV NON-TUNL • REPL CATH CV NON-TNL W/O PORT – Arterial Procedures • INSERTION CATHETER, ARTE • ARTERIAL PUNCTURE – Transcatheter Procedures • INS ENDOVAS VENA CAVA FILTR • Implants – Pacemakers • INSERT PACING ELECTRODE,LT VEN • INSERT PULSE GENERATOR
  26. 26. Urinary Procedures • Endoscopy – CYSTOENDOSCOPY • Introduction Procedures – CATH BLADDER SMP
  27. 27. Wounds • Superficial Wounds – REPAIR SUPERFICIAL WOUND – RPR LAC SMP SCALP 12.6-20.0CM • Active Wound Care Management – REMOVE DEVITALIZED WOUND TISSU – NEG PRESS WOUND TX > 50 SQCM
  28. 28. References • 1. Papadimitriou-Olivgeris M, Marangos M, Fligou F, Christofidou M, Bartzavali C, Anastassiou ED, Filos KS. 2012. Risk factors for KPC-producing Klebsiella pneumoniae enteric colonization upon ICU admission. J Antimicrob Chemother 67:2976-81. • 2. Schwartz-Neiderman A, Braun T, Fallach N, Schwartz D, Carmeli Y, Schechner V. 2016. Risk Factors for Carbapenemase-Producing Carbapenem-Resistant Enterobacteriaceae (CP-CRE) Acquisition Among Contacts of Newly Diagnosed CP-CRE Patients. Infect Control Hosp Epidemiol 37:1219-25. • 3. Ben-David D, Masarwa S, Navon-Venezia S, Mishali H, Fridental I, Rubinovitch B, Smollan G, Carmeli Y, Schwaber MJ, Group IPCP-A-CFC-RKpW. 2011. Carbapenem-resistant Klebsiella pneumoniae in post-acute-care facilities in Israel. Infect Control Hosp Epidemiol 32:845-53. • 4. Mathers AJ, Cox HL, Bonatti H, Kitchel B, Brassinga AK, Wispelwey B, Sawyer RG, Pruett TL, Hazen KC, Patel JB, Sifri CD. 2009. Fatal cross infection by carbapenem-resistant Klebsiella in two liver transplant recipients. Transpl Infect Dis 11:257-65. • 5. Enfield KB, Huq NN, Gosseling MF, Low DJ, Hazen KC, Toney DM, Slitt G, Zapata HJ, Cox HL, Lewis JD, Kundzins JR, Mathers AJ, Sifri CD. 2014. Control of simultaneous outbreaks of carbapenemase-producing enterobacteriaceae and extensively drug-resistant Acinetobacter baumannii infection in an intensive care unit using interventions promoted in the Centers for Disease Control and Prevention 2012 carbapenemase-resistant Enterobacteriaceae Toolkit. Infect Control Hosp Epidemiol 35:810-7. • 6. Mathers AJ, Poulter M, Dirks D, Carroll J, Sifri CD, Hazen KC. 2014. Clinical Microbiology Costs for Methods of Active Surveillance for Klebsiella pneumoniae Carbapenemase-Producing Enterobacteriaceae. Infect Control Hosp Epidemiol 35:350-5. • 7. Sheppard AE, Stoesser N, Wilson DJ, Sebra R, Kasarskis A, Anson LW, Giess A, Pankhurst LJ, Vaughan A, Grim CJ, Cox HL, Yeh AJ, Sifri CD, Walker AS, Peto TE, Crook DW, Mathers AJ, Group MMMMI. 2016. Nested Russian Doll-Like Genetic Mobility Drives Rapid Dissemination of the Carbapenem Resistance Gene blaKPC. Antimicrob Agents Chemother 60:3767-78. • 8. CDC. 2009. Guidance for control of infections with carbapenem-resistant or carbapenemase-producing Enterobacteriaceae in acute care facilities. MMWR Morb Mortal Wkly Rep 58:256-60 • 9. Hussein K, Sprecher H, Mashiach T, Oren I, Kassis I, Finkelstein R. 2009. Carbapenem resistance among Klebsiella pneumoniae isolates: risk factors, molecular characteristics, and susceptibility patterns. Infect Control Hosp Epidemiol 30:666 -71. • 10. Pereira MR, Scully BF, Pouch SM, Uhlemann AC, Goudie S, Emond JE, Verna EC. 2015. Risk factors and outcomes of carbapenem-resistant Klebsiella pneumoniae infections in liver transplant recipients. Liver Transpl 21:1511-9. • 11. O'Horo JC, Farrell A, Sohail MR, Safdar N. 2016. Carbapenem-resistant Enterobacteriaceae and endoscopy: An evolving threat. Am J Infect Control 44:1032-6. • 12. Patel G, Huprikar S, Factor SH, Jenkins SG, Calfee DP. 2008. Outcomes of carbapenem-resistant Klebsiella pneumoniae infection and the impact of antimicrobial and adjunctive therapies. Infect Control Hosp Epidemiol 29:1099-106. • 13. Ng AY. 2002. On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes., vol 2, p 841-848, Advances in neural information processing systems. • 14. DMW P. 2011. Evaluation: From Precision, Recall and F-M easure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies 2:37-63.

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