Thirty-day readmissions and reoperations after total elbow arthroplasty: a national database study

Cutler, Holt S.; Collett, Garen; Farahani, Farzam; Ahn, Juhno; Nakonezny, Paul A.; Koehler, Daniel M.; Khazzam, Michael

doi:10.1016/j.jse.2020.06.033

Cited by 14 publications

(5 citation statements)

References 18 publications

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“…This is consistent with other published studies which have shown diabetes as a risk factor for readmission following total hip and knee arthroplasty 42 but not following total elbow or shoulder arthroplasty. 43,44 The exact reasoning for this finding is unclear and further studies are needed to better define the role of diabetes, perioperative glucose control, and target thresholds for hemoglobin A1c on readmission after joint arthroplasty.…”

Section: Discussionmentioning

confidence: 99%

Shared and unique risk factors for readmission exist following upper and lower extremity arthroplasty in the 30-day postoperative period

Scholten

Gwam

Recker

et al. 2023

J Orthop Surg (Hong Kong)

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Purpose Joint arthroplasty has become increasingly more common in the United States, and it is important to examine the patient-based risk factors and surgical variables associated with hospital readmissions. The purpose of this study was to identify stratified rates and risk factors for readmission after upper extremity (shoulder, elbow, and wrist) and lower extremity (hip, knee, and ankle) arthroplasty. Methods All patients undergoing upper and lower extremity arthroplasty from 2008–2018 were identified using the National Surgical Quality Improvement Program dataset. Patient demographics, medical comorbidities and surgical characteristics were examined utilizing uni- and multi-variate analysis for significant predictors of 30-days hospital readmission. Results A total of 523,523 lower and 25,215 upper extremity arthroplasty patients were included in this study. A number of 22,183 (4.2%) lower and 1072 (4.4%) upper extremity arthroplasty patients were readmitted within 30 days of discharge. Significant risk factors for 30-days readmission after lower extremity arthroplasty included age, Body Mass Index (BMI), operative time, dependent functional status, American Society of Anesthesiologists (ASA) score ≥3, increased length of stay, and various medical comorbidities such as diabetes, tobacco dependency, and chronic obstructive pulmonary disease (COPD). An overweight BMI was associated with a lower odds of 30-days readmission when compared to a normal BMI for lower extremity arthroplasty. Analysis for upper extremity arthroplasty revealed similar findings of significant risk factors for 30-days hospital readmission, although diabetes mellitus was not found to be a significant risk factor. Conclusion Nearly one in 25 patients undergoing upper and lower extremity arthroplasty experiences hospital readmission within 30-days of index surgery. There are several modifiable risk factors for 30-days hospital readmission shared by both lower and upper extremity arthroplasty, including tobacco smoking, COPD, and hypertension. Optimization of these medical comorbidities may mitigate the risk short-term readmission following joint arthroplasty procedures and improve overall cost effectiveness of perioperative surgical care.

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Section: Discussionmentioning

confidence: 99%

Shared and unique risk factors for readmission exist following upper and lower extremity arthroplasty in the 30-day postoperative period

Scholten

Gwam

Recker

et al. 2023

J Orthop Surg (Hong Kong)

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“…Postoperative infection is also an important reason for unplanned readmissions, such as lung infection requiring anti-infective treatment or wound infection requiring readmission for debridement or surgery. An increase in BMI is closely associated with higher incidence of hypertension, coronary heart disease, and diabetes, while reduced BMI, on the other hand, is also a sign of malnutrition and frailty status in the elderly ( Graboyes et al, 2018 ; Sperling et al, 2018 ; Workman et al, 2020 ; Cutler et al, 2021 ). Hyperbilirubinemia reflects underlying hemolysis and hepatic dysfunction.…”

Section: Discussionmentioning

confidence: 99%

Machine learning prediction of postoperative unplanned 30-day hospital readmission in older adult

Wang²,

Li³

et al. 2022

Front. Mol. Biosci.

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Background: Although unplanned hospital readmission is an important indicator for monitoring the perioperative quality of hospital care, few published studies of hospital readmission have focused on surgical patient populations, especially in the elderly. We aimed to investigate if machine learning approaches can be used to predict postoperative unplanned 30-day hospital readmission in old surgical patients.Methods: We extracted demographic, comorbidity, laboratory, surgical, and medication data of elderly patients older than 65 who underwent surgeries under general anesthesia in West China Hospital, Sichuan University from July 2019 to February 2021. Different machine learning approaches were performed to evaluate whether unplanned 30-day hospital readmission can be predicted. Model performance was assessed using the following metrics: AUC, accuracy, precision, recall, and F1 score. Calibration of predictions was performed using Brier Score. A feature ablation analysis was performed, and the change in AUC with the removal of each feature was then assessed to determine feature importance.Results: A total of 10,535 unique surgeries and 10,358 unique surgical elderly patients were included. The overall 30-day unplanned readmission rate was 3.36%. The AUCs of the six machine learning algorithms predicting postoperative 30-day unplanned readmission ranged from 0.6865 to 0.8654. The RF + XGBoost algorithm overall performed the best with an AUC of 0.8654 (95% CI, 0.8484–0.8824), accuracy of 0.9868 (95% CI, 0.9834–0.9902), precision of 0.3960 (95% CI, 0.3854–0.4066), recall of 0.3184 (95% CI, 0.259–0.3778), and F1 score of 0.4909 (95% CI, 0.3907–0.5911). The Brier scores of the six machine learning algorithms predicting postoperative 30-day unplanned readmission ranged from 0.3721 to 0.0464, with RF + XGBoost showing the best calibration capability. The most five important features of RF + XGBoost were operation duration, white blood cell count, BMI, total bilirubin concentration, and blood glucose concentration.Conclusion: Machine learning algorithms can accurately predict postoperative unplanned 30-day readmission in elderly surgical patients.

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“… 17 , 18 Few studies have demonstrated an association between lower BMIs and 30-day postoperative adverse events. 19 It is possible that the association of a lower BMI with reoperation is confounded by an unobserved variable. For instance, a lower BMI may represent malnutrition; unfortunately, data for preoperative serum albumin are incomplete in the NSQIP database, precluding the analysis of this variable in our study.…”

Section: Discussionmentioning

confidence: 99%

Factors Associated with 30-Day Adverse Events After Brachial Plexus Neurolysis

Zhang

Garg

Elhassan

et al. 2022

Journal of Hand Surgery Global Online

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Thirty-day readmissions and reoperations after total elbow arthroplasty: a national database study

Cited by 14 publications

References 18 publications

Shared and unique risk factors for readmission exist following upper and lower extremity arthroplasty in the 30-day postoperative period

Shared and unique risk factors for readmission exist following upper and lower extremity arthroplasty in the 30-day postoperative period

Machine learning prediction of postoperative unplanned 30-day hospital readmission in older adult

Factors Associated with 30-Day Adverse Events After Brachial Plexus Neurolysis

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