Risk-prediction for postoperative major morbidity in coronary surgery☆

Antunes, Pedro E.; Oliveira, J. Ferrão de; Antunes, Manuel J.

doi:10.1016/j.ejcts.2008.10.046

Cited by 34 publications

(57 citation statements)

References 20 publications

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“…Sex was included in only 6% (1/16) of models predicting any outcome among patients with hemorrhagic stroke, as compared to 24% (6/25) of such models among ischemic stroke patients. Study‐ and model‐level characteristics of the 30 stroke‐related CPMs that included sex are presented in Table 2 25, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54…”

Section: Resultsmentioning

confidence: 99%

Field Synopsis of the Role of Sex in Stroke Prediction Models

Paulus

Lai

Lundquist

et al. 2016

JAHA

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BackgroundGuidelines for stroke prevention recommend development of sex‐specific stroke risk scores. Incorporating sex in Clinical Prediction Models (CPMs) may support sex‐specific clinical decision making. To better understand their potential to guide sex‐specific care, we conducted a field synopsis of the role of sex in stroke‐related CPMs.Methods and ResultsWe identified stroke‐related CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Database, a systematic summary of cardiovascular CPMs published from January 1990 to May 2012. We report the proportion of models including the effect of sex on stroke incidence or prognosis, summarize the directionality of the predictive effects of sex, and explore factors influencing the inclusion of sex. Of 92 stroke‐related CPMs, 30 (33%) contained a coefficient for sex or presented sex‐stratified models. Only 12/58 (21%) CPMs predicting outcomes in patients included sex, compared to 18/30 (60%) models predicting first stroke (P<0.0001). Sex was most commonly included in models predicting stroke among a general population (69%). Female sex was consistently associated with reduced mortality after ischemic stroke (n=4) and higher risk of stroke from arrhythmias or coronary revascularization (n=5). Models predicting first stroke versus outcomes among patients with stroke (odds ratio=5.75, 95% CI 2.18–15.14, P<0.001) and those developed from larger versus smaller sample sizes (odds ratio=4.58, 95% CI 1.73–12.13, P=0.002) were significantly more likely to include sex.ConclusionsSex is included in a minority of published CPMs, but more frequently in models predicting incidence of first stroke. The importance of sex‐specific care may be especially well established for primary prevention.

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Section: Resultsmentioning

confidence: 99%

Field Synopsis of the Role of Sex in Stroke Prediction Models

Paulus

Lai

Lundquist

et al. 2016

JAHA

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“…Regarding the preoperative risk factors for postoperative pulmonary functions, this factor was not a main determinant of postoperative risk status. The particular time of the surgery; during the day, during the week or during the year, was not related to poor respiratory outcomes in elective CABG (3)(4)(5). So, this would not be considered as a risk factor for CABG patients.…”

Section: Operating Room Management Factorsmentioning

confidence: 99%

“…Therefore, patients with a preoperative forced expiratory volume of less than 70%, are at greater risk (3). Patients with preoperative ventilation problems also have increased risk factors for postoperative problems (12).…”

Section: Preexisting Chronic Obstructive Pulmonary Diseasementioning

confidence: 99%

“…Postoperative atrial fibrillation (AF) has been shown to be a risk factor for prediction of post-operative long-term mortality, therefore, using warfarin as an anticoagulant could improve survival (3,31). The main reason why underlying AF could aggravate the respiratory outcome relies on a number of factors, including; increased pulmonary arterial pressure due to impaired contraction of the left atrium, increased risk for production of atrial clots, especially in the left atrial appendage (LAA), lack of effective contractile force of the LAA and a multitude of other proposed mechanisms (31-33, 35).…”

Section: Preexisting Arrhythmiasmentioning

confidence: 99%

“…Risk factor identification and assessment would improve the quality of clinical management of these patients much more accurately (1,2). The optimization of clinical conditions before the start of the surgical procedure might help to enhance the clinical outcomes for these patients (3,4), and could also help clinicians in their clinical management, thus preventing untoward postoperative events and complications. In addition, cardiovascular surgical teams have to be aware of potential risk factors predisposing patients to unacceptable postoperative outcomes (5).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Risk Factors for Postoperative Respiratory Mortality and Morbidity in Patients Undergoing Coronary Artery Bypass Grafting

Rajaei

Dabbagh²

2012

anesthpain

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Characterization of Risk Prediction Models for Acute Kidney Injury

et al. 2023

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ImportanceDespite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes.ObjectiveTo systematically review published AKI prediction models across all clinical subsettings.Data SourcesMEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models.Study SelectionAll studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs.Data Extraction and SynthesisTwo authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias.Main Outcomes and MeasuresC statistic was used to measure the discrimination of prediction models.ResultsOf the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium–associated AKI: I2 = 99.9%; P &lt; .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool.Conclusions and RelevanceIn this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.

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Risk-prediction for postoperative major morbidity in coronary surgery☆

Abstract: We developed a set of risk-prediction models that can be used as an instrument to provide information to clinicians and patients about the risk of postoperative major morbidity in our patient population undergoing isolated CABG.

Cited by 34 publications

References 20 publications

Field Synopsis of the Role of Sex in Stroke Prediction Models

Field Synopsis of the Role of Sex in Stroke Prediction Models

Risk Factors for Postoperative Respiratory Mortality and Morbidity in Patients Undergoing Coronary Artery Bypass Grafting

Characterization of Risk Prediction Models for Acute Kidney Injury

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