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Structural, Nursing, and Physician Characteristics and 30-Day Mortality for Patients Undergoing Cardiac Surgery in Pennsylvania.

ABSTRACT: OBJECTIVE:Cardiac surgery ICU characteristics and clinician staffing patterns have not been well characterized. We sought to describe Pennsylvania cardiac ICUs and to determine whether ICU characteristics are associated with mortality in the 30 days after cardiac surgery. DESIGN:From 2012 to 2013, we conducted a survey of cardiac surgery ICUs in Pennsylvania to assess ICU structure, care practices, and clinician staffing patterns. ICU data were linked to an administrative database of cardiac surgery patient discharges. We used logistic regression to measure the association between ICU variables and death in 30 days. SETTING:Cardiac surgery ICUs in Pennsylvania. PATIENTS:Patients having coronary artery bypass grafting and/or cardiac valve repair or replacement from 2009 to 2011. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:Of the 57 cardiac surgical ICUs in Pennsylvania, 43 (75.4%) responded to the facility survey. Rounds included respiratory therapists in 26 of 43 (60.5%) and pharmacists in 23 of 43 (53.5%). Eleven of 41 (26.8%) reported that at least 2/3 of their nurses had a bachelor's degree in nursing. Advanced practice providers were present in most of the ICUs (37/43; 86.0%) but residents (8/42; 18.6%) and fellows (7/43; 16.3%) were not. Daytime intensivists were present in 21 of 43 (48.8%) responding ICUs; eight of 43 (18.6%) had nighttime intensivists. Among 29,449 patients, there was no relationship between mortality and nurse ICU experience, presence of any intensivist, or absence of residents after risk adjustment. To exclude patients who may have undergone transcatheter aortic valve replacement, we conducted a subgroup analysis of patients undergoing only coronary artery bypass grafting, and results were similar. CONCLUSIONS:Pennsylvania cardiac surgery ICUs have variable structures, care practices, and clinician staffing, although none of these are statistically significantly associated with mortality in the 30 days following surgery after adjustment.

SUBMITTER: Lane-Fall MB

PROVIDER: S-EPMC5561002 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Structural, Nursing, and Physician Characteristics and 30-Day Mortality for Patients Undergoing Cardiac Surgery in Pennsylvania.

Lane-Fall Meghan B MB Ramaswamy Tara S TS Brown Sydney E S SES He Xu X Gutsche Jacob T JT Fleisher Lee A LA Neuman Mark D MD

Critical care medicine 20170901 9

<h4>Objective</h4>Cardiac surgery ICU characteristics and clinician staffing patterns have not been well characterized. We sought to describe Pennsylvania cardiac ICUs and to determine whether ICU characteristics are associated with mortality in the 30 days after cardiac surgery.<h4>Design</h4>From 2012 to 2013, we conducted a survey of cardiac surgery ICUs in Pennsylvania to assess ICU structure, care practices, and clinician staffing patterns. ICU data were linked to an administrative database ...[more]

PMID: 28661969

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Project description:ImportanceQuality improvement programs for colorectal cancer surgery have been introduced with benchmarking based on quality indicators, such as mortality. Detailed (pre)operative characteristics may offer relevant information for proper case-mix correction.ObjectiveTo investigate the added value of machine learning to predict quality indicators for colorectal cancer surgery and identify previously unrecognized predictors of 30-day mortality based on a large, nationwide colorectal cancer registry that collected extensive data on comorbidities.Design, setting, and participantsAll patients who underwent resection for primary colorectal cancer registered in the Dutch ColoRectal Audit between January 1, 2011, and December 31, 2016, were included. Multiple machine learning models (multivariable logistic regression, elastic net regression, support vector machine, random forest, and gradient boosting) were made to predict quality indicators. Model performance was compared with conventionally used scores. Risk factors were identified by logistic regression analyses and Shapley additive explanations (ie, SHAP values). Statistical analysis was performed between March 1 and September 30, 2020.Main outcomes and measuresThe primary outcome of this cohort study was 30-day mortality. Prediction models were trained on a training set by performing 5-fold cross-validation, and outcomes were measured by the area under the receiver operating characteristic curve on the test set. Machine learning was further used to identify risk factors, measured by odds ratios and SHAP values.ResultsThis cohort study included 62 501 records, most patients were male (35 116 [56.2%]), were aged 61 to 80 years (41 560 [66.5%]), and had an American Society of Anesthesiology score of II (35 679 [57.1%]). A 30-day mortality rate of 2.7% (n = 1693) was found. The area under the curve of the best machine learning model for 30-day mortality (0.82; 95% CI, 0.79-0.85) was significantly higher than the American Society of Anesthesiology score (0.74; 95% CI, 0.71-0.77; P < .001), Charlson Comorbidity Index (0.66; 95% CI, 0.63-0.70; P < .001), and preoperative score to predict postoperative mortality (0.73; 95% CI, 0.70-0.77; P < .001). Hypertension, myocardial infarction, chronic obstructive pulmonary disease, and asthma were comorbidities with a high risk for increased mortality. Machine learning identified specific risk factors for a complicated course, intensive care unit admission, prolonged hospital stay, and readmission. Laparoscopic surgery was associated with a decreased risk for all adverse outcomes.Conclusions and relevanceThis study found that machine learning methods outperformed conventional scores to predict 30-day mortality after colorectal cancer surgery, identified specific patient groups at risk for adverse outcomes, and provided directions to optimize benchmarking in clinical audits.

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Dataset Information

Structural, Nursing, and Physician Characteristics and 30-Day Mortality for Patients Undergoing Cardiac Surgery in Pennsylvania.

Publications

Structural, Nursing, and Physician Characteristics and 30-Day Mortality for Patients Undergoing Cardiac Surgery in Pennsylvania.

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