Clinical prediction tool for 30-day readmissions in home OPAT I. Bengoetxea¹, A.Apezetxea², D.De Damborenea³, E.Altuna⁴, C.Garde ⁵MJ. Onaindia¹, M. Fernandez Martinez de Mardojana⁶, JM. Quintana ⁷ , U. Aguirre⁷, M.Berroete⁸, M.Millet⁹ and de TADE-SVHAD group Basque Health Service; Integrated Health Organization (IHO) 1.IHO Barrualde-Galdakao; Hospital at Home; 2. IHO -Basurto; Hospital at Home; 3. IHO Ezkerraldea--Cruces; Hospital at Home; 4. IHO Araba; Hospital at Home; 5. IHO ; Hospital at Home; 6. IHO Mendaro; Hospital at Home; 7. IHO Barrualde-Galdakao Research Unit; 8. IHO Alto Urola: Hospital at home; 9. IHO Bidasoa: Hospital at home.

Growth for 2004 DERIVATION VALIDATION BACKGROUND RESULTS Variables Beta (s.e.) OR (95% IC) p weight for the Beta (s.e.) OR (95% IC) p score Intravenous antibiotic administration at the patient's home is a safe and effective Mean patient age was 63 years (range, 11-102 years), 58.74% were men, and the most Month of income January-february-march 1.02 (0.45) 2.78 (1.14, 6.77) 0.02 2 1.15 (0.48) 3.17 (1.23, 8.17) 0.02 Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec technique in the treatment of many infectious diseases. There are several studies that common diagnoses were urinary tract infection (23%), respiratory infections (16.9%), and April-may 0.46 (0.53) 1.59 (0.56, 4.50) 0.39 0 0.87 (0.54) 2.38 (0.83, 6.84) 0.11 show that some variables can be identified as prognostic factors these patients. The main diverticulitis (14.2%).. 51.5% did not present any complication 29.6% showed a single June-july-august- - Reference - 0 - Reference - goal of this study is to identifying predictor variables and create into easy-to-use predictive complication (being the most frequent fever) and 18.2% had more than one complication september october-november- 0.46 (0.46) 1.59 (0.64, 3.95) 0.32 0 1.44 (0.48) 4.20 (1.64, 10.77) 0.003 rules focusing on readmission 30 days after discharge . during the evolution of home hospitalization. The readmission rate during the december hospitalization episode at home was 8.67%. Of these readmissions, 72% were related to Admissions mode the infectious pathology by which they income to the unit of hospitalization at home and Hospitalizatión 1.32 (0.35) 3.76 (1.89, 7.47) <0.001 3 1.37 (0.32) 3.92 (2.10, 7.30) <0.001 27.90% were related to another pathology of the patient's. The unplanned 30-day Emergency room - Reference 0 - Reference others 1.34 (0.49) 3.84 (1.46, 10.10) 0.007 3 0.61 (0.48) 1.85 (0.72, 4.72) 0.20 OBJECTIVES readmission rate was 12.3%. risk factor for infection 0 - Reference - 0 - Reference - To identify risk factors for poor outcome in patients with intravenous home antibiotic 1 0.85 (0.31) 2.34 (1.28, 4.27) 0.006 2 0.19 (0.27) 1.22 (0.71, 2.07) 0.47 treatment at home hospitalization. THE VARIABLES RELATED TO READMISSION AT 2 1.57 (0.32) 4.83 (2.58, 9.04) <0.001 4 0.82 (0.30) 2.28 (1.26, 4.12) 0.006 -Develop and validate predictive rules for each of the situations 30 DAYS IN VALIDATION SAMPLE WERE: who handles venous -Gender ( p=0.03) acces sanitary - Reference 0 - Reference -Age (p=0.009) not rearmissions Sanitary & caregiver 1.19 (0.28) 3.29 (1.91, 5.67) <0.001 3 1.01 (0.28) 2.75 (1.58, 4.79) <0.001

-Month of income 0 risk factors for AUC (95% IC) 0.787 (0.740, 0.834) 0.733 (0.682, 0.784) infection disease -Risk factors for infection (p=<0.0001) 1 risk factor for infection disease METHODS -Charlson index (p<0.0001) ≥2 risk factors for infection disease -Signs of sepsis during admissions (p=0.05) 1488 outpatients with parenteral antibiotic therapy at home treated at 8 hospital at home CONCLUSIONS -Admissions mode (p=<0.0001) units in Spain over 1 years (from 10/10/2012 to 30/09/2013) wereas prospectively -Number of microorganism (p= 0.012) Patients prescribed outpatient parenteral antibiotic treatment are at risk for readmission. recruited. More than 120 variables collected, included patient demographics, co -Presence of multirresistance (p=0.01) Our readmission rate is lower compered to the series previously presented, this may be morbidities, infections, antibiotic classes, therapy characteristics, unplanned early -Who handles venous acces (p=0.0003) due to our setting (home hospitalization), in which the patients have a greater medical admissions, 30-day hospital admissions, causes and risk factors of complications. -Type of venous acces (<0.0001) surveillance than in other outpatient parenteral models. Age, Charlson index or drug- Statistical analysis: 1) Creation of the predictive model in derivation group (50% of the readmissions at 30 days -Infusion mode (p=0.0013) 0 risk factors for infection resistance are development risk for OPAT readmission however finally they have not been overall sample). Univariate analysis to identify which variables, of the possible predictors, disease -Type of antibiotic (p=0.0096) 1 risk factor for infection in our predictive model. The annual seasonality is one of the variables that enter in our are related to the variable of "readmission up to 30 days". The categories of the variables disease -Income proteine level (p=0.04 ) ≥2 risk factors for model and these make us think that probably has some reason related to the health of this model have been assigned a weight in relation to the parameter β obtained in this infection disease -Total days of treatment (p=0.0009) system itself that has not been studied. Because de reasons for readmissions multivariate model. This will give us a total score. From this scale risk categories have arehetereogeneous, prediction of this event may be more difficult. However the use and been created. 2) Validation of the predictive model in the validation sample. Sensitivity, development of this type of tools will allow us to establish corrective measures, to avoid specificity and area under the curve were obtained by comparing them with the results late readmissions. obtained in the shunt sample. The calibration capacity of the models has been evaluated Localization of infection by readmissions Modality of admission to hospital at home using the Hosmer-Lemeshow test. The ability to discriminate using ROC curveThis study setting is part of a larger one that encompassing predictive rules for all variables of poor evolution 30 25 respiratory non rearmissions readmisssions at 30 days REFERENCES 20 genitourinary 78,89 in OPAT: mild and severe complications, reentry during the administration of antibiotic 15 1Tice A, Rehn S, et al. Practice guidelines for Outpatient Parenteral Antimicrobial Therapy. Clin infect Dis. 2004;38:1651-721. 2.-Galperiné et al. Outpatient PArenteral antimicrobial therapy (OPAT) in bone and joint 49,7 11,11 10 abdominal 43,71 10 infections. Med Mal Infect. 2006;36: 132-7 5.-Wiliams DN. Comunita-Based Parenteral Anti-infective Therapy (Co PAT). Pharmakocinetic and Monitoring Issues. Clin Pharmacokinet 1998; 35: 65-77. 3.-Dall L et Al. 5 Hospitalist TReatment of CAP and cellulitis Using Objetive Criteria to select Patients. Infect Med 2003; 20: 379-90. 4.-Nathwani D et al. Advisory group on Home BAsed and Outpatient care (AdHOC): an internacional death skin and sof tissues 6,59 0 consensus statement on non-inpatient parenteral therapy CMI 2000;6:464-76. 5.-Tice AD. Documenting the value of OPAT: Outcome studies and patient registries. Can infect Dis 2000; 10 Suppl: 45A-48A.. 6.- osteoarticular no readmission readmission 30 hospitalization Nathwani D, Tice A. Ambulatory antimicrobial use : the value of a outcome registry. JAC 2002; 49:149-54. 7.-MIron M, Estrada O, Gonzalez Ramallo V. Protocolos TADE; Editorial Elsevier 2008. 8.-Estrada O . Anàlisi days emergency dels indicadors de seguretat i eficàcia per a l'avaluació del programa de tractament antibiòtic endovenòs d'una unitat d'hospitalització a domicili. 9.-hoffman -terry M. Frainmow H.,Fox T et al. Adverse effects of outpatient room others parenteral antibiotic therapy. The American journal of medicina. 1999 (106) 44-49. 10.-Allison M., Muldoon E., Kent D et al. Prediction Model for 30-day hospital readmissións among patients discrharges receiving outpatient parenteral antibiotic therapy. CID 2014: 58; 812-819.

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