Background
Many patients having discretionary orthopaedic surgery take opioids daily, either with a prescription or illicitly, however little is known regarding the prevalence and effect of high-risk ...opioid use (eg, abuse, dependence) in the perioperative orthopaedic setting.
Questions/purposes
We sought (1) to determine the prevalence of opioid abuse and dependence in patients undergoing major elective orthopaedic surgery; (2) to characterize the relationship of opioid abuse and dependence with in-hospital postoperative mortality and adverse events, failure to rescue, prolonged length of stay, and nonroutine disposition; and (3) to identify factors associated with high-risk opioid use.
Methods
We used coding data collected in discharge records from the Nationwide Inpatient Sample (2002–2011). We analyzed changes with time in the prevalence of opioid abuse and dependence on admission. Finally, we used multivariate regression modeling to measure the association of opioid abuse and dependence with in-hospital postoperative mortality, morbidity, and resource utilization, and to identify factors associated with high-risk opioid use.
Results
The prevalence of opioid abuse and dependence increased from 0.095% in 2002 to 0.24% in 2011, an increase of 152% (p < 0.001). Opioid abuse and dependence were associated with increased inpatient mortality (odds ratio OR, 3.7; 95% CI, 2.7–5.1) and aggregate morbidity (OR, 2.3 l; 95% CI, 2.2–2.4), including induced mental disorder (OR, 5.9; 95% CI, 5.4–6.3), respiratory failure (OR, 3.1; 95% CI, 2.7–3.6), surgical site infection (OR, 2.5; 95% CI, 2.0–3.0), mechanical ventilation (OR, 2.3; 95% CI, 2.0–2.5), pneumonia (OR, 2.1; 95% CI, 1.8–2.3), myocardial infarction (OR, 1.9; 95% CI, 1.3–2.6), and postoperative ileus or other gastrointestinal events (OR, 1.4; 95% CI, 1.3–1.6) (p < 0.001 for all listed entities). Abuse and dependence also were associated with increased risk for prolonged hospital length of stay (OR, 2.5; 95% CI, 2.4–2.5), nonroutine discharge (OR, 2.2; 95% CI, 2.2–2.3), and failure to rescue (OR, 2.0; 95% CI, 1.4–2.8). High-risk opioid users were more likely to be younger, male, nonwhite, Medicaid-insured patients with mental health and substance use disorders, and to be undergoing spine surgery. Hospital-related characteristics included urban setting, geographic location in the Northeast or West, and serving as a teaching facility.
Conclusion
Opioid abuse and dependence are increasing rapidly among orthopaedic surgical inpatients and are associated with considerable postoperative morbidity and mortality and resource utilization. We recommend that orthopaedic surgeons screen patients carefully for opioid misuse preoperatively, help patients who are using opioids inappropriately to discontinue them before scheduling elective surgery, decline to perform elective surgery in patients who misuse opioids, and closely monitor patients who are habituated to opioids at the time they undergo surgery.
Level of Evidence
Level III, prognostic study.
Background
Scores derived from comorbidities can help with risk adjustment of quality and safety data. The Charlson and Elixhauser comorbidity measures are well-known risk adjustment models, yet the ...optimal score for orthopaedic patients remains unclear.
Questions/purposes
We determined whether there was a difference in the accuracy of the Charlson and Elixhauser comorbidity-based measures in predicting (1) in-hospital mortality after major orthopaedic surgery, (2) in-hospital adverse events, and (3) nonroutine discharge.
Methods
Among an estimated 14,007,813 patients undergoing orthopaedic surgery identified in the National Hospital Discharge Survey (1990–2007), 0.80% died in the hospital. The association of each Charlson comorbidity measure and Elixhauser comorbidity measure with mortality was assessed in bivariate analysis. Two main multivariable logistic regression models were constructed, with in-hospital mortality as the dependent variable and one of the two comorbidity-based measures (and age, sex, and year of surgery) as independent variables. A base model that included only age, sex, and year of surgery also was evaluated. The discriminative ability of the models was quantified using the area under the receiver operating characteristic curve (AUC). The AUC quantifies the ability of our models to assign a high probability of mortality to patients who die. Values range from 0.50 to 1.0, with 0.50 indicating no ability to discriminate and 1.0 indicating perfect discrimination.
Results
Elixhauser comorbidity adjustment provided a better prediction of in-hospital case mortality (AUC, 0.86; 95% CI, 0.86–0.86) compared with the Charlson model (AUC, 0.83; 95% CI, 0.83–0.84) and to the base model with no comorbidities (AUC, 0.81; 95% CI, 0.81–0.81). In terms of relative improvement in predictive performance, the Elixhauser measure performed 60% better than the Charlson score in predicting mortality. The Elixhauser model discriminated inpatient morbidity better than the Charlson measure, but the discriminative ability of the model was poor and the difference in the absolute improvement in predictive power between the two models (AUC, 0.01) is of dubious clinical importance. Both comorbidity models exhibited the same degree of discrimination for estimating nonroutine discharge (AUC, 0.81; 95% CI, 0.81–0.82 for both models).
Conclusions
Provider-specific outcomes, particularly inpatient mortality, may be evaluated differently depending on the comorbidity risk adjustment model selected. Future research assessing and comparing the performance of the Charlson and Elixhauser measures in predicting long-term outcomes would be of value.
Level of Evidence
Level II, prognostic study. See the Instructions for Authors for a complete description of levels of evidence.
Health Literacy and Decisional Preferences in Hand Surgery Tarabochia, Matthew; Menendez, Mariano E.; Ring, David
The Journal of hand surgery (American ed.),
January 2021, 2021-Jan, 2021-01-00, 20210101, Letnik:
46, Številka:
1
Journal Article
Recenzirano
Patients increasingly prefer to be involved in health decisions. Shared decisions are associated with less decisional conflict and improved satisfaction. This study examines the relationship between ...health literacy and preferred decision-making role in patients seeking care for hand problems.
We performed a cross-sectional study of 226 patients who were presenting to a hand service for the first time, who were older than 18 years, not pregnant, and fluent and literate in English. Patient demographic information, work status, and education level were collected from the patient. Diagnosis and insurance status were collected from the medical record. Pain interference, depression, and upper extremity function were measured using the Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaires administered with computerized adaptive testing. Health literacy and preferred decision-making role were assessed using the Newest Vital Sign Test and Control Preference Scale, respectively. Discordance between preferred and actual decision-making role was assessed using the Control Preference Scale statements with the words “I prefer” omitted. Bivariate and multivariable logistic regressions were used to assess for factors associated with preferred decision-making role and discordance.
Seventy-eight percent of patients preferred an active or collaborative, rather than a passive, role in decision making. Sixty-one percent felt they had achieved their preferred role. Preferred role and achievement of that role were not associated with health literacy, age, race, insurance, work status, diagnosis, pain intensity, symptoms of depression, and magnitude of self-reported activity limitations. More years of education was associated with a more active preferred decision-making role but not with obtaining that preferred role.
Most people want to participate in decisions about their care even if they have low health literacy.
Hand specialists can seek strategies to help people understand and participate in care decisions.
Humans are one of the few species undergoing an adolescent growth spurt. Because children enter the spurt at different ages making age a poor maturity measure, longitudinal studies are necessary to ...identify the growth patterns and identify commonalities in adolescent growth. The standard maturity determinant, peak height velocity (PHV) timing, is difficult to estimate in individuals due to diurnal, postural, and measurement variation. Using prospective longitudinal populations of healthy children from two North American populations, we compared the timing of the adolescent growth spurt's peak height velocity to normalized heights and hand skeletal maturity radiographs. We found that in healthy children, the adolescent growth spurt is standardized at 90% of final height with similar patterns for children of both sexes beginning at the initiation of the growth spurt. Once children enter the growth spurt, their growth pattern is consistent between children with peak growth at 90% of final height and skeletal maturity closely reflecting growth remaining. This ability to use 90% of final height as easily identified important maturity standard with its close relationship to skeletal maturity represents a significant advance allowing accurate prediction of future growth for individual children and accurate maturity comparisons for future studies of children's growth.
Background
As payment models shift toward a focus on value and reimbursement becomes increasingly tied to quality and patient experience, minimizing unexpected acute health needs has become a ...priority for both policymakers and clinical leaders. Despite recent emphasis on emergency department (ED) visits as a quality measure in surgery, little is known about the role of the ED in the early postoperative period after hand surgery.
Questions/purposes
The purpose of this study was to determine the rates, reasons, and factors associated with ED visits within 30 days of elective outpatient hand surgery.
Methods
Using our institutional database for 2009 through 2013, we assessed ED visit rates for 2332 patients undergoing carpal tunnel or trigger finger release. Medical records were manually reviewed to ascertain the primary reason for the ED visit. Multivariable logistic regression modeling was used to identify factors independently associated with ED use.
Results
A total of 67 patients (3%) experienced at least one ED visit within 30 days of hand surgery (carpal tunnel: 3%; trigger finger: 3%). Most visits (66%) occurred within the first 2 weeks of surgery, and 31% led to hospitalization. The most common reasons for ED visits were pain (18%) and wound issues (16%). Unmarried and medically infirm patients were more likely to visit the ED.
Conclusions
ED visits after hand surgery are common, often related to the procedure, and potentially responsive to quality improvement initiatives. Targeted efforts to educate patients about pain management, wound care, and the expected course of recovery before surgery, together with close postoperative contact (eg, routine phone calls, facsimile correspondence by email, or secure messaging) may limit visits to the ED.
Level of Evidence
Level IV, prognostic study.
OBJECTIVES:Proximal humerus fractures are very common in infirm elderly patients and are associated with appreciable inpatient mortality. We sought to compare the discriminative ability of the ...Charlson and Elixhauser comorbidity measures for predicting inpatient mortality after proximal humerus fractures.
METHODS:Data from the Nationwide Inpatient Sample (2002–2011) were obtained. We constructed 2 main multivariable logistic regression models, with inpatient mortality as the dependent variable and 1 of the 2 comorbidity scores, as well as age and sex, as independent variables. A base model that contained only age and sex was also evaluated. The predictive performance of the Charlson and Elixhauser comorbidity measures was assessed and compared using the area under the receiver operating characteristic curve (AUC) derived from these regression models.
RESULTS:Elixhauser comorbidity adjustment provided better discrimination of inpatient mortality AUC = 0.840, 95% confidence interval (CI), 0.828–0.853 than the Charlson model (AUC = 0.786, 95% CI, 0.771–0.801) and the base model without comorbidity adjustment (AUC = 0.722, 95% CI, 0.705–0.740). In terms of relative improvement in predictive ability, the Elixhauser score performed 46% better than the Charlson score.
CONCLUSIONS:Given that inadequate comorbidity risk adjustment can unfairly penalize hospitals and surgeons that care for a disproportionate share of infirm and sick patients, wider adoption of the Elixhauser measure for mortality prediction after proximal humerus fracture—and perhaps other musculoskeletal injuries—merits to be considered.
LEVEL OF EVIDENCE:Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.
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