ABSTRACT Introduction: Atrial fibrillation is the main complication in the postoperative period of cardiovascular surgery. Its genesis is multifactorial, so its rapid identification to mitigate the associated risks is essential. Objective: To evaluate the incidence of atrial fibrillation in patients undergoing coronary artery bypass grafting (CABG) and its relationship with other complications in our setting. Methods: This is a multicenter, observational study involving patients undergoing isolated CABG between 2017 and 2019 with data from the Registro Paulista de Cirurgia Cardiovascular (or REPLICCAR II). Variables were prospectively collected in REDCap following the definitions given by version 2.73 of the Society of Thoracic Surgeons Adult Cardiac Surgery Database. Data were collected with prior authorization from the local ethics committee and analyses performed in R software. Results: A total of 3,803 patients were included, of these 605 had postoperative atrial fibrillation (POAF). In order to adjust the groups, propensity score matching was used. Such analyses resulted in 605 patients in each group (without POAF vs. with POAF). Among patients with POAF, the mean age was 67.56 years, with a prevalence of males (73.6%, 445 patients). Patients belonging to the group with POAF had a mortality rate of 9.26% (P=0.007), longer ventilation time (P<0.001), pneumonia (P<0.001), and sepsis (P<0.001). In multiple analysis, acute renal dysfunction (P=0.032) and longer intensive care unit stay (P<0,001) were associated with the presence of POAF. Conclusion: POAF in CABG is associated with longer intensive care unit and hospital stay, as well as renal dysfunction, pneumonia, and in-hospital mortality. Introduction surgery multifactorial essential Objective (CABG setting Methods multicenter 201 or II. II . II) 273 2 73 2.7 Database software Results 3803 3 803 3,80 included 60 (POAF) groups used without vs POAF) 6756 67 56 67.5 years 73.6%, 736 6 (73.6% 44 patients. patients) 926 9 26 9.26 P=0.007, P0007 P P=0.007 , 0 007 (P=0.007) P<0.001, P0001 P<0.001 001 (P<0.001) P<0.001. analysis P=0.032 P0032 032 (P=0.032 P<0,001 (P<0,001 Conclusion inhospital 20 27 7 2. 380 80 3,8 (POAF 675 5 67. 73.6% (73.6 4 92 9.2 P000 P=0.00 00 (P=0.007 P<0.00 (P<0.001 P=0.03 P003 03 (P=0.03 P<0,00 (P<0,00 38 8 3, 73.6 (73. 9. P00 P=0.0 (P=0.00 P<0.0 (P<0.00 (P=0.0 P<0,0 (P<0,0 73. (73 P0 P=0. P<0. (P<0.0 (P=0. P<0, (P<0, (7 P=0 P<0 (P<0. (P=0 (P<0 ( P= P< (P= (P< (P

Resumo Fundamento: A análise de indicadores como taxa de readmissão hospitalar é crucial para aprimorar a qualidade dos serviços e gestão em processos hospitalares. Objetivo: Identificar as variáveis correlacionadas a readmissão hospitalar até 30 dias após cirurgia de revascularização miocárdica (CRM). Métodos: Estudo de coorte transversal no banco de dados Registro Paulista de Cirurgia Cardiovascular II (REPLICCAR II)(N=3.392), de junho de 2017 a junho de 2019. Avaliaram-se retrospectivamente 150 pacientes para identificar os fatores correlacionados a readmissão hospitalar até 30 dias após-CRM via regressão logística univariada e multivariada. As análises foram realizadas no software R, com significância de 0,05 e intervalos de confiança de 95%. Resultados: Cento e cinquenta pacientes foram readmitidos até 30 dias após a alta hospitalar de CRM (150/3.392, 4,42%) principalmente por infecções (mediastinite, ferida operatória e sepse) totalizando 52 casos (52/150, 34,66%), outras causas foram: complicações cirúrgicas (14/150, 9,33%) e pneumonia (13/150, 8,66%). Os preditores de readmissão identificados foram: O modelo de regressão multivariada apontou intercepto (OR: 1,098, p<0,00001), apneia do sono (OR: 1,117, p=0,0165), arritmia cardíaca (OR: 1,040, p=0,0712) e uso de balão intra-aórtico (OR: 1,068, p=0,0021) como preditores do desfecho, com uma AUC de 0,70. Conclusão: 4,42% dos pacientes foram readmitidos pós-CRM, principalmente por infecções. Fatores como apneia do sono (OR: 1,117, p=0,0165), arritmia cardíaca (OR: 1,040, p=0,0712) e uso de balão intra-aórtico (OR: 1,068, p=0,0021) foram preditores de readmissão, com uma discriminação de risco moderada (AUC: 0,70). Fundamento hospitalares Objetivo 3 CRM. . (CRM) Métodos REPLICCAR IIN=3.392, IIN3392 IIN N=3.392 , N 392 II)(N=3.392) 201 2019 Avaliaramse Avaliaram se 15 apósCRM R 005 0 05 0,0 95 95% Resultados 150/3.392, 1503392 (150/3.392 442 4 42 mediastinite, mediastinite (mediastinite sepse 5 52/150, 52150 (52/150 34,66%, 3466 34,66% 34 66 34,66%) 14/150, 14150 14 (14/150 9,33% 933 9 33 13/150, 13150 13 (13/150 8,66%. 866 8,66% 8 8,66%) OR (OR 1098 1 098 1,098 p<0,00001, p000001 p p<0,00001 00001 p<0,00001) 1117 117 1,117 p=0,0165, p00165 p=0,0165 0165 p=0,0165) 1040 040 1,040 p=0,0712 p00712 0712 intraaórtico intra aórtico 1068 068 1,068 p=0,0021 p00021 0021 desfecho 070 70 0,70 Conclusão 4,42 pósCRM, pósCRM pós CRM, pós-CRM (AUC 0,70) (CRM IIN=3.392 IIN339 N3392 N=3.39 39 II)(N=3.392 20 00 0, 150/3.392 150339 (150/3.39 44 52/150 5215 (52/15 346 34,66 6 14/150 1415 (14/15 9,33 93 13/150 1315 (13/15 86 8,66 109 09 1,09 p00000 p<0,0000 0000 111 11 1,11 p0016 p=0,016 016 104 04 1,04 p=0,071 p0071 071 106 06 1,06 p=0,002 p0002 002 07 7 0,7 4,4 IIN=3.39 IIN33 N339 N=3.3 II)(N=3.39 2 150/3.39 15033 (150/3.3 52/15 521 (52/1 34,6 14/15 141 (14/1 9,3 13/15 131 (13/1 8,6 10 1,0 p0000 p<0,000 000 1,1 p001 p=0,01 01 p=0,07 p007 p=0,00 p000 4, IIN=3.3 IIN3 N33 N=3. II)(N=3.3 150/3.3 1503 (150/3. 52/1 (52/ 34, 14/1 (14/ 9, 13/1 (13/ 8, 1, p<0,00 p00 p=0,0 IIN=3. N3 N=3 II)(N=3. 150/3. (150/3 52/ (52 14/ (14 13/ (13 p<0,0 p0 p=0, IIN=3 N= II)(N=3 150/3 (150/ (5 (1 p<0, p=0 IIN= II)(N= 150/ (150 ( p<0 p= II)(N (15 p<

Abstract Background: The analysis of indicators such as hospital readmission rates is crucial for improving the quality of services and management of hospital processes. Objectives: To identify the variables correlated with hospital readmission up to 30 days following coronary artery bypass grafting (CABG). Methods: Cross-sectional cohort study by REPLICCAR II database (N=3,392) from June 2017 to June 2019. Retrospectively, 150 patients were analyzed to identify factors associated with hospital readmission within 30 days post-CABG using univariate and multivariate logistic regression. Analysis was conducted using software R, with a significance level of 0.05 and 95% confidence intervals. Results: Out of 3,392 patients, 150 (4,42%0 were readmitted within 30 days post-discharge from CABG primarily due to infections (mediastinitis, surgical wounds, and sepsis) accounting for 52 cases (34.66%). Other causes included surgical complications (14/150, 9.33%) and pneumonia (13/150, 8.66%). The multivariate regression model identified an intercept (OR: 1.098, p<0.00001), sleep apnea (OR: 1.117, p=0.0165), cardiac arrhythmia (OR: 1.040, p=0.0712), and intra-aortic balloon pump use (OR: 1.068, p=0.0021) as predictors of the outcome, with an AUC of 0.70. Conclusion: 4.42% of patients were readmitted post-CABG, mainly due to infections. Factors such as sleep apnea (OR: 1.117, p=0.0165), cardiac arrhythmia (OR: 1.040, p=0.0712), and intra-aortic balloon pump use (OR: 1.068, p=0.0021) were predictors of readmission, with moderate risk discrimination (AUC: 0.70). Background processes Objectives 3 CABG. . (CABG) Methods Crosssectional Cross sectional N=3,392 N3392 N 392 (N=3,392 201 2019 Retrospectively 15 postCABG post R 005 0 05 0.0 95 intervals Results 3392 3,39 4,42%0 4420 4 42 (4,42% postdischarge discharge mediastinitis, mediastinitis (mediastinitis wounds sepsis 5 34.66%. 3466 34.66% 34 66 (34.66%) 14/150, 14150 14 (14/150 9.33% 933 9 33 13/150, 13150 13 (13/150 8.66%. 866 8.66% 8 8.66%) OR (OR 1098 1 098 1.098 p<0.00001, p000001 p p<0.00001 , 00001 p<0.00001) 1117 117 1.117 p=0.0165, p00165 p=0.0165 0165 p=0.0165) 1040 040 1.040 p=0.0712, p00712 p=0.0712 0712 p=0.0712) intraaortic intra aortic 1068 068 1.068 p=0.0021 p00021 0021 outcome 070 70 0.70 Conclusion 442 4.42 postCABG, CABG, (AUC 0.70) (CABG N=3,39 N339 39 (N=3,39 20 00 0. 339 3,3 4,42% (4,42 346 34.66 6 (34.66% 14/150 1415 (14/15 9.33 93 13/150 1315 (13/15 86 8.66 109 09 1.09 p00000 p<0.0000 0000 111 11 1.11 p0016 p=0.016 016 104 04 1.04 p0071 p=0.071 071 106 06 1.06 p=0.002 p0002 002 07 7 0.7 44 4.4 N=3,3 N33 (N=3,3 2 3, 4,42 (4,4 34.6 (34.66 14/15 141 (14/1 9.3 13/15 131 (13/1 8.6 10 1.0 p0000 p<0.000 000 1.1 p001 p=0.01 01 p007 p=0.07 p=0.00 p000 4. N=3, N3 (N=3, 4,4 (4, 34. (34.6 14/1 (14/ 9. 13/1 (13/ 8. 1. p<0.00 p00 p=0.0 N=3 (N=3 4, (4 (34. 14/ (14 13/ (13 p<0.0 p0 p=0. N= (N= ( (34 (1 p<0. p=0 (N (3 p<0 p= p<

ABSTRACT The aim of this study was to investigate the effect of gels containing the monoterpene borneol in induced oral mucositis using an animal model. Gels were prepared with borneol at 1.2% and 2.4% (w/w). Oral mucositis was induced by administration of three doses of 5-fluorouracil (30 mg/kg, i.p.) and injury with acetic acid (50%, v/v) soaked in filter paper applied to right cheek mucosa for 60s. Four subgroups comprising 12 animals each were formed. Six animals from each group were sacrificed at days seven and fourteen after oral mucositis induction. Mucous samples were processed and stained with hematoxylin-eosin and Masson’s Trichrome. The semiquantitative evaluation involved observation of inflammatory parameters. ImageJ® software was used in the quantitative evaluation. For statistical analyses, Two-way ANOVA, followed by Tukey’s post-test (p <0.05), were employed. Borneol 2.4% gel proved effective in the treatment of oral mucositis with statistically significant differences between groups for angiogenesis control, inflammatory cell count reduction and percentage neoformed collagen increase. The confirmation of anti-inflammatory and healing action of borneol in oral mucositis in rats renders it a good marker for predicting this activity for plant extracts rich in this substance.