Postoperative complications and implications on patient-centered outcomes

Tevis, Sarah E.; Kennedy, Gregory D.

doi:10.1016/j.jss.2013.01.032

Cited by 154 publications

(117 citation statements)

References 28 publications

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“…It is also frequently used as a proxy for quality of care for surgical patients and may come under increased scrutiny as our healthcare delivery paradigm continues to evolve. Postoperative complications are known to increase the length of stay significantly [25]; however, our analysis shows preoperative MI to be an independent predictor of prolonged length of stay even after additional adjustment for complications (five days vs. four days).…”

Section: Discussioncontrasting

confidence: 62%

Impact of preoperative myocardial infarction on surgical outcomes in inpatient orthopaedic surgery

Lim

Edelstein

Jain

et al. 2013

International Orthopaedics (SICOT)

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Purpose The purpose of this study was to evaluate the impact of a preoperative myocardial infarction (MI) on outcomes of inpatient orthopaedic operations. Methods The National Surgical Quality Improvement Program database was used to identify patients who underwent common orthopaedic operations from 2006 to 2010. Patient demographic data, comorbidities, complications, and lengths of stay were collected. Multivariate logistic regression and linear regression models were used to compare outcomes for patients with and without a history of MI in the six months prior to surgery. Results Of the 32,462 patients identified, 86 had sustained an MI in the six months prior to surgery. The MI cohort had no cardiac complications but had increased incidences of superficial surgical site infection, unplanned re-intubation, ventilator-assisted respiration for more than 48 hours, pneumonia, sepsis or septic shock, and postoperative mortality within 30 days of surgery, as well as prolonged lengths of stay. Following logistic regression to adjust for baseline differences, a history of MI showed no association with cardiac complications and was significantly associated with superficial surgical site infection (OR 3.6, 95 % CI 1.1-11.8), ventilator dependence for over 48 hours (OR 4.0, 95 % CI 1.1-14.0), and extended length of stay (median with interquartile range 4 [4-4] vs. 5 days).Conclusions A myocardial infarction within six months prior to orthopaedic surgery is not associated with a higher risk of 30-day perioperative cardiac complications; however, it is associated with increased rates of surgical site infection, prolonged ventilator dependence, and longer hospital stay.

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Section: Discussioncontrasting

confidence: 62%

Impact of preoperative myocardial infarction on surgical outcomes in inpatient orthopaedic surgery

Lim

Edelstein

Jain

et al. 2013

International Orthopaedics (SICOT)

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“…The perioperative period contains significant risk, where clinical instability is the norm more than the exception 1,2 . Up to 43% of surgical patients may exhibit some kind of perioperative complication [3][4][5][6][7][8][9][10][11][12][13][14][15][16] , and short-term morbidities are associated with longer-term outcomes. A recently published manuscript has demonstrated that perioperative mortality is the third leading cause of death internationally 17 .…”

Section: Introductionmentioning

confidence: 99%

“…Although perioperative care can help prevent these complications 4,18 , clinicians often struggle to identify those patients at highest risk of complications without performing time-consuming chart reviews 19 . This has led to the adoption of risk scoring systems 20,21 ; however, most current risk scores are focused on individual complications 22,23 , and tend to use simplistic point systems to allow for easy application 21,22 .…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set

et al. 2020

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During the perioperative period patients often suffer complications, including acute kidney injury (AKI), reintubation, and mortality. In order to effectively prevent these complications, high-risk patients must be readily identified. However, most current risk scores are designed to predict a single postoperative complication and often lack specificity on the patient level. In other fields, machine learning (ML) has been shown to successfully create models to predict multiple end points using a single input feature set. We hypothesized that ML can be used to create models to predict postoperative mortality, AKI, reintubation, and a combined outcome using a single set of features available at the end of surgery. A set of 46 features available at the end of surgery, including drug dosing, blood loss, vital signs, and others were extracted. Additionally, six additional features accounting for total intraoperative hypotension were extracted and trialed for different models. A total of 59,981 surgical procedures met inclusion criteria and the deep neural networks (DNN) were trained on 80% of the data, with 20% reserved for testing. The network performances were then compared to ASA Physical Status. In addition to creating separate models for each outcome, a multitask learning model was trialed that used information on all outcomes to predict the likelihood of each outcome individually. The overall rate of the examined complications in this data set was 0.79% for mortality, 22.3% (of 21,676 patients with creatinine values) for AKI, and 1.1% for reintubation. Overall, there was significant overlap between the various model types for each outcome, with no one modeling technique consistently performing the best. However, the best DNN models did beat the ASA score for all outcomes other than mortality. The highest area under the receiver operating characteristic curve (AUC) models were 0.792 (0.775-0.808) for AKI, 0.879 (0.851-0.905) for reintubation, 0.907 (0.872-0.938) for mortality, and 0.874 (0.864-0.866) for any outcome. The ASA score alone achieved AUCs of 0.652 (0.636-0.669) for AKI, 0.787 (0.757-0.818) for reintubation, 0.839 (0.804-0.875) for mortality, and 0.76 (0.748-0.773) for any outcome. Overall, the DNN architecture was able to create models that outperformed the ASA physical status to predict all outcomes based on a single feature set, consisting of objective data available at the end of surgery. No one model architecture consistently performed the best.

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“…4 Despite advances made in understanding factors that elevate surgical risk and the widespread uptake of quality improvement initiatives, limited progress has been made in circumventing increased hospital costs of care that directly result from postoperative morbidity. Patient-centered outcomes of care are emerging as a metric within various surgical disciplines, 5 providing an opportunity to evaluate contributors to increased costs of care, one of which is the cost of hospital readmissions. In June 2009, the Centers for Medicare and Medicaid Services began acknowledging rates of 30-day readmission, thereby highlighting the importance of hospital readmission as an important quality metric.…”

mentioning

confidence: 99%

Risk Factors and Indications for 30-Day Readmission After Primary Surgery for Epithelial Ovarian Cancer

AlHilli¹,

Langstraat²,

Tran

et al. 2015

International Journal of Gynecological Cancer

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Background To identify patients at risk for postoperative morbidities, we evaluated indications and factors associated with 30-day readmission after epithelial ovarian cancer surgery. Methods Patients undergoing primary surgery for epithelial ovarian cancer between January 2, 2003, and December 29, 2008, were evaluated. Univariable and multivariable logistic regression models were fit to identify factors associated with 30-day readmission. A parsimonious multivariable model was identified using backward and stepwise variable selection. Results In total, 324 (60.2%) patients were stage III and 91 (16.9%) were stage IV. Of all 538 eligible patients, 104 (19.3%) were readmitted within 30 days. Cytoreduction to no residual disease was achieved in 300 (55.8%) patients, and 167 (31.0%) had measurable disease (≤1 cm residual disease). The most common indications for readmission were surgical site infection (SSI; 21.2%), pleural effusion/ascites management (14.4%), and thromboembolic events (12.5%). Multivariate analysis identified American Society of Anesthesiologists score of 3 or higher (odds ratio, 1.85; 95% confidence interval, 1.18–2.89; P = 0.007), ascites [1.76 (1.11–2.81); P = 0.02], and postoperative complications during initial admission [grade 3–5 vs none, 2.47 (1.19–5.16); grade 1 vs none, 2.19 (0.98–4.85); grade 2 vs none, 1.28 (0.74–2.21); P = 0.048] to be independently associated with 30-day readmission (c-index = 0.625). Chronic obstructive pulmonary disease was the sole predictor of readmission for SSI (odds ratio, 3.92; 95% confidence interval, 1.07–4.33; P = 0.04). Conclusions Clinically significant risk factors for 30-day readmission include American Society of Anesthesiologists score of 3 or higher, ascites and postoperative complications at initial admission. The SSI and pleural effusions/ascites are common indications for readmission. Systems can be developed to predict patients needing outpatient management, improve care, and reduce costs.

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Postoperative complications and implications on patient-centered outcomes

Cited by 154 publications

References 28 publications

Impact of preoperative myocardial infarction on surgical outcomes in inpatient orthopaedic surgery

Impact of preoperative myocardial infarction on surgical outcomes in inpatient orthopaedic surgery

Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set

Risk Factors and Indications for 30-Day Readmission After Primary Surgery for Epithelial Ovarian Cancer

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