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<
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<