scholarly journals Preoperative clinical model to predict myocardial injury after non-cardiac surgery: a retrospective analysis from the MANAGE cohort in a Spanish hospital

BMJ Open ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. e045052
Author(s):  
Ana Belen Serrano ◽  
Maria Gomez-Rojo ◽  
Eva Ureta ◽  
Monica Nuñez ◽  
Borja Fernández Félix ◽  
...  

ObjectivesTo determine preoperative factors associated to myocardial injury after non-cardiac surgery (MINS) and to develop a prediction model of MINS.DesignRetrospective analysis.SettingTertiary hospital in Spain.ParticipantsPatients aged ≥45 years undergoing major non-cardiac surgery and with at least two measures of troponin levels within the first 3 days of the postoperative period. All patients were screened for the MANAGE trial.Primary and secondary outcome measuresWe used multivariable logistic regression analysis to study risk factors associated with MINS and created a score predicting the preoperative risk for MINS and a nomogram to facilitate bed-side use. We used Least Absolute Shrinkage and Selection Operator method to choose the factors included in the predictive model with MINS as dependent variable. The predictive ability of the model was evaluated. Discrimination was assessed with the area under the receiver operating characteristic curve (AUC) and calibration was visually assessed using calibration plots representing deciles of predicted probability of MINS against the observed rate in each risk group and the calibration-in-the-large (CITL) and the calibration slope. We created a nomogram to facilitate obtaining risk estimates for patients at pre-anaesthesia evaluation.ResultsOur cohort included 3633 patients recruited from 9 September 2014 to 17 July 2017. The incidence of MINS was 9%. Preoperative risk factors that increased the risk of MINS were age, American Status Anaesthesiology classification and vascular surgery. The predictive model showed good performance in terms of discrimination (AUC=0.720; 95% CI: 0.69 to 0.75) and calibration slope=1.043 (95% CI: 0.90 to 1.18) and CITL=0.00 (95% CI: −0.12 to 0.12).ConclusionsOur predictive model based on routinely preoperative information is highly affordable and might be a useful tool to identify moderate-high risk patients before surgery. However, external validation is needed before implementation.

2001 ◽  
Vol 45 (5) ◽  
pp. 543-549 ◽  
Author(s):  
A. Mayr ◽  
H. Knotzer ◽  
W. Pajk ◽  
G. Luckner ◽  
N. Ritsch ◽  
...  

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e049089
Author(s):  
Marcia C Castro ◽  
Susie Gurzenda ◽  
Eduardo Marques Macário ◽  
Giovanny Vinícius A França

ObjectiveTo provide a comprehensive description of demographic, clinical and radiographic characteristics; treatment and case outcomes; and risk factors associated with in-hospital death of patients hospitalised with COVID-19 in Brazil.DesignRetrospective cohort study of hospitalised patients diagnosed with COVID-19.SettingData from all hospitals across Brazil.Participants522 167 hospitalised patients in Brazil by 14 December 2020 with severe acute respiratory illness, and a confirmed diagnosis for COVID-19.Primary and secondary outcome measuresPrevalence of symptoms and comorbidities was compared by clinical outcomes and intensive care unit (ICU) admission status. Survival was assessed using Kaplan Meier survival estimates. Risk factors associated with in-hospital death were evaluated with multivariable Cox proportional hazards regression.ResultsOf the 522 167 patients included in this study, 56.7% were discharged, 0.002% died of other causes, 30.7% died of causes associated with COVID-19 and 10.2% remained hospitalised. The median age of patients was 61 years (IQR, 47–73), and of non-survivors 71 years (IQR, 60–80); 292 570 patients (56.0%) were men. At least one comorbidity was present in 64.5% of patients and in 76.8% of non-survivors. From illness onset, the median times to hospital and ICU admission were 6 days (IQR, 3–9) and 7 days (IQR, 3–10), respectively; 15 days (IQR, 9–24) to death and 15 days (IQR, 11–20) to hospital discharge. Risk factors for in-hospital death included old age, Black/Brown ethnoracial self-classification, ICU admission, being male, living in the North and Northeast regions and various comorbidities. Age had the highest HRs of 5.51 (95% CI: 4.91 to 6.18) for patients≥80, compared with those ≤20.ConclusionsCharacteristics of patients and risk factors for in-hospital mortality highlight inequities of COVID-19 outcomes in Brazil. As the pandemic continues to unfold, targeted policies that address those inequities are needed to mitigate the unequal burden of COVID-19.


2021 ◽  
Vol 23 ◽  
pp. 100158
Author(s):  
Yazmín Guillén Dolores ◽  
Carlos Alberto Delgado Quintana ◽  
Gustavo Lugo Goytia

2016 ◽  
Vol 23 (2) ◽  
pp. 99-109 ◽  
Author(s):  
Donata Ringaitienė ◽  
Dalia Gineitytė ◽  
Vaidas Vicka ◽  
Tadas Žvirblis ◽  
Jūratė Šipylaitė ◽  
...  

Background. Malnutrition (MN) is prevalent in cardiac surgery, but there are no specific preoperative risk factors of MN. The aim of this study is to assess the clinically relevant risk factors of MN for cardiac surgery patients. Materials and methods. The nutritional state of the patients was evaluated one day prior to surgery using a bioelectrical impedance analysis phase angle (PA). Two groups of patients were generated according to low PA: malnourished and well nourished. Risk factors of MN were divided into three clinically relevant groups: psychosocial and lifestyle factors, laboratory findings and disease-associated factors. Variables in each different group were entered into separate multivariate logistic regression models. Results. A total of 712 patients were included in the study. The majority of them were 65-year old men after a CABG procedure. Low PA was present in 22.9% (163) of patients. The analysis of disease-related factors of MN revealed the importance of heart functions (NYHA IV class OR: 3.073, CI95%: 1.416–6.668, p = 0.007), valve pathology (OR: 1.825, CI95%: 1.182–2.819, p = 0.007), renal insufficiency (OR: 4.091, CI95%: 1.995–8.389, p 


2020 ◽  
Vol 195 ◽  
pp. 105828
Author(s):  
Royce W. Woodroffe ◽  
Logan C. Helland ◽  
Andrew J. Grossbach ◽  
Kirill V. Nourski ◽  
Patrick W. Hitchon

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