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Association Between Wait Time and 30-Day Mortality in Adults Undergoing Hip Fracture Surgery.

ABSTRACT: Importance:Although wait times for hip fracture surgery have been linked to mortality and are being used as quality-of-care indicators worldwide, controversy exists about the duration of the wait that leads to complications. Objective:To use population-based wait-time data to identify the optimal time window in which to conduct hip fracture surgery before the risk of complications increases. Design, Setting, and Participants:Population-based, retrospective cohort study of adults undergoing hip fracture surgery between April 1, 2009, and March 31, 2014, at 72 hospitals in Ontario, Canada. Risk-adjusted restricted cubic splines modeled the probability of each complication according to wait time. The inflection point (in hours) when complications began to increase was used to define early and delayed surgery. To evaluate the robustness of this definition, outcomes among propensity-score matched early and delayed surgical patients were compared using percent absolute risk differences (RDs, with 95% CIs). Exposure:Time elapsed from hospital arrival to surgery (in hours). Main Outcomes and Measures:Mortality within 30 days. Secondary outcomes included a composite of mortality or other medical complications (myocardial infarction, deep vein thrombosis, pulmonary embolism, and pneumonia). Results:Among 42 230 patients with hip fracture (mean [SD] age, 80.1 years [10.7], 70.5% women) who met study entry criteria, overall mortality at 30 days was 7.0%. The risk of complications increased when wait times were greater than 24 hours, irrespective of the complication considered. Compared with 13 731 propensity-score matched patients who received surgery earlier, 13 731 patients who received surgery after 24 hours had a significantly higher risk of 30-day mortality (898 [6.5%] vs 790 [5.8%]; % absolute RD, 0.79; 95% CI, 0.23-1.35) and the composite outcome (1680 [12.2%]) vs 1383 [10.1%]; % absolute RD, 2.16; 95% CI, 1.43-2.89). Conclusions and Relevance:Among adults undergoing hip fracture surgery, increased wait time was associated with a greater risk of 30-day mortality and other complications. A wait time of 24 hours may represent a threshold defining higher risk.

SUBMITTER: Pincus D

PROVIDER: S-EPMC5820694 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeThe 30-day mortality rate after hip fracture surgery has been considered as an indirect indicator of the quality of care. The aim of this work is to analyse preoperative and postoperative factors potentially related to early 30-day mortality in patients over 65 undergoing hip fracture surgery.MethodsProspective cohort study including all consecutive primary hip fracture patients over 65 admitted to Trauma and Orthopaedics department from January 1, 2018 to December 31, 2019. Bed-ridden, non- surgically treated patients, and high energy trauma or tumoral aetiology fractures were excluded. A total of 943 patients were eligible (attrition rate: 2.1%). Follow-up included 30-days after discharge. We noted the 30-day mortality after hip fracture surgery, analysing 130 potentially related variables including biodemographic, fracture-related, preoperative, and postoperative clinical factors. Qualitative variables were assessed by χ2, and quantitative variables by non-parametric tests. Odds ratio determined by binary logistic regression. We selected preventable candidate variables for multivariate risk assessment by logistic regression.ResultsA total of 923 patients were enrolled (mean age 86.22±6.8, 72.9% women). The 30-day mortality rate was 6.0%. We noted significant increased mortality on men (OR = 2.381[1.371-4.136], p = 0.002), ageing patients (ORyear = 1.073[1.025-1.122], p = 0.002), and longer time to surgery (ORday = 1.183[1.039-1146], p<0.001), on other 20 preoperative clinical variables, like lymphopenia (lymphocyte count <103/μl, OR = 1.842[1.063-3.191], p = 0.029), hypoalbuminemia (≤3.5g/dl, OR = 2.474[1.316-4.643], p = 0.005), and oral anticoagulant intake (OR = 2.499[1.415-4.415], p = 0.002), and on 25 postoperative clinical variables, like arrhythmia (OR = 13.937[6.263-31.017], p<0.001), respiratory insufficiency (OR = 7.002[3.947-12.419], p<0.001), hyperkalaemia (OR = 10.378[3.909-27.555], p<0.001), nutritional supply requirement (OR = 3.576[1.894-6.752], p = 0.021), or early arthroplasty dislocation (OR = 6.557[1.206-35.640], p = 0.029). We developed a predictive model for early mortality after hip fracture surgery based on postoperative factors with 96.0% sensitivity and 60.7% specificity (AUC = 0.863).ConclusionWe revealed that not only preoperative, but also postoperative factors have a great impact after hip fracture surgery. The influence of post-operative factors on 30-day mortality has a logical basis, albeit so far they have not been identified or quantified before. Our results provide an advantageous picture of the 30-day mortality after hip fracture surgery.

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.

Project description:BackgroundHealth care funding reforms are being used worldwide to improve system performance but may invoke unintended consequences. We assessed the effects of introducing a targeted hospital funding model, based on fixed price and volume, for hip fractures. We hypothesized the policy change was associated with reduction in wait times for hip fracture surgery, increase in wait times for non-hip fracture surgery, and increase in the incidence of after-hours hip fracture surgery.MethodsThis was a population-based, interrupted time series analysis of 49,097 surgeries for hip fractures, 10,474 for ankle fractures, 1,594 for tibial plateau fractures, and 40,898 for appendectomy at all hospitals in Ontario, Canada between April 2012 and March 2017. We used segmented regression analysis of interrupted monthly time series data to evaluate the impact of funding reform enacted April 1, 2014 on wait time for hip fracture repair (from hospital presentation to surgery) and after-hours provision of surgery (occurring between 1700 and 0700 h). To assess potential adverse consequences of the reform, we also evaluated two control procedures, ankle and tibial plateau fracture surgery. Appendectomy served as a non-orthopedic tracer for assessment of secular trends.ResultsThe difference (95 % confidence interval) between the actual mean wait time and the predicted rate had the policy change not occurred was - 0.46 h (-3.94 h, 3.03 h) for hip fractures, 1.46 h (-3.58 h, 6.50 h) for ankle fractures, -3.22 h (-39.39 h, 32.95 h) for tibial plateau fractures, and 0.33 h (-0.57 h, 1.24 h) for appendectomy (Figure 1; Table 3). The difference (95 % confidence interval) between the actual and predicted percentage of surgeries performed after-hours - 0.90 % (-3.91 %, 2.11 %) for hip fractures, -3.54 % (-11.25 %, 4.16 %) for ankle fractures, 7.09 % (-7.97 %, 22.14 %) for tibial plateau fractures, and 1.07 % (-2.45 %, 4.59 %) for appendectomy.ConclusionsWe found no significant effects of a targeted hospital funding model based on fixed price and volume on wait times or the provision of after-hours surgery. Other approaches for improving hip fracture wait times may be worth pursuing instead of funding reform.

Project description: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.

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Association Between Wait Time and 30-Day Mortality in Adults Undergoing Hip Fracture Surgery.

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