A validated preoperative risk prediction tool for extended inpatient length of stay following anatomic or reverse total shoulder arthroplasty.

BACKGROUND: Recent work has shown inpatient length of stay (LOS) following shoulder arthroplasty to hold the second strongest association with overall cost (after implant cost itself). In particular, a preoperative understanding for the patients at risk of extended inpatient stays (≥3 days) can allow for counseling, optimization, and anticipating postoperative adverse events. METHODS: A multicenter retrospective review was performed of 5410 anatomic (52%) and reverse (48%) total shoulder arthroplasties done at 2 large, tertiary referral health systems. The primary outcome was extended inpatient LOS of at least 3 days, and over 40 preoperative sociodemographic and comorbidity factors were tested for their predictive ability in a multivariable logistic regression model based on the patient cohort from institution 1 (derivation, N = 1773). External validation was performed using the patient cohort from institution 2 (validation, N = 3637), including area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values. RESULTS: A total of 814 patients, including 318 patients (18%) in the derivation cohort and 496 patients (14%) in the validation cohort, experienced an extended inpatient LOS of at least 3 days. Four hundred forty-five (55%) were discharged to a skilled nursing or rehabilitation facility. Following parameter selection, a multivariable logistic regression model based on the derivation cohort (institution 1) demonstrated excellent preliminary accuracy (AUC: 0.826), with minimal decrease in accuracy under external validation when tested against the patients from institution 2 (AUC: 0.816). The predictive model was composed of only preoperative factors, in descending predictive importance as follows: age, marital status, fracture case, ASA (American Society of Anesthesiologists) score, paralysis, electrolyte disorder, body mass index, gender, neurologic disease, coagulation deficiency, diabetes, chronic pulmonary disease, peripheral vascular disease, alcohol dependence, psychoses, smoking status, and revision case. CONCLUSION: A freely-available, preoperative online clinical decision tool for extended inpatient LOS (≥ 3 days) after shoulder arthroplasty reaches excellent predictive accuracy under external validation. As a result, this tool merits consideration for clinical implementation, as many risk factors are potentially modifiable as part of a preoperative optimization strategy.

Appropriate patient selection for outpatient shoulder arthroplasty: a risk prediction tool.

BACKGROUND: The transition from inpatient to outpatient shoulder arthroplasty critically depends on appropriate patient selection, both to ensure safety and to counsel patients preoperatively regarding individualized risk. Cost and patient demand for same-day discharge have encouraged this transition, and a validated predictive tool may help decrease surgeon liability for complications and help select patients appropriate for same-day discharge. We hypothesized that an accurate predictive model could be created for short inpatient length of stay (discharge at least by postoperative day 1), potentially serving as a useful proxy for identifying patients appropriate for true outpatient shoulder arthroplasty. METHODS: A multicenter cohort of 5410 shoulder arthroplasties (2805 anatomic and 2605 reverse shoulder arthroplasties) from 2 geographically diverse, high-volume health systems was reviewed. Short inpatient stay was the primary outcome, defined as discharge on either postoperative day 0 or 1, and 49 patient outcomes and factors including the Elixhauser Comorbidity Index, sociodemographic factors, and intraoperative parameters were examined as candidate predictors for a short stay. Factors surviving parameter selection were incorporated into a multivariable logistic regression model, which underwent internal validation using 10,000 bootstrapped samples. RESULTS: In total, 2238 patients (41.4%) were discharged at least by postoperative day 1, with no difference in rates of 90-day readmission (3.5% vs. 3.3%, P = .774) between cohorts with a short length of stay and an extended length of stay (discharge after postoperative day 1). A multivariable logistic regression model demonstrated high accuracy (area under the receiver operator characteristic curve, 0.762) for discharge by postoperative day 1 and was composed of 13 variables: surgery duration, age, sex, electrolyte disorder, marital status, American Society of Anesthesiologists score, paralysis, diabetes, neurologic disease, peripheral vascular disease, pulmonary circulation disease, cardiac arrhythmia, and coagulation deficiency. The percentage cutoff maximizing sensitivity and specificity was calculated to be 47%. Internal validation showed minimal loss of accuracy after bias correction for overfitting, and the predictive model was incorporated into a freely available online tool to facilitate easy clinical use. CONCLUSIONS: A risk prediction tool for short inpatient length of stay after shoulder arthroplasty reaches very good accuracy despite requiring only 13 variables and was derived from an underlying database with broad geographic diversity in the largest institutional shoulder arthroplasty cohort published to date. Short inpatient length of stay may serve as a proxy for identifying patients appropriate for same-day discharge, although perioperative care decisions should always be made on an individualized and holistic basis.

A validated preoperative risk prediction tool for discharge to skilled nursing or rehabilitation facility following anatomic or reverse shoulder arthroplasty.

BACKGROUND: As bundled payment models continue to spread, understanding the primary drivers of cost excess helps providers avoid penalties and ensure equal health care access. Recent work has shown discharge to rehabilitation and skilled nursing facilities (SNFs) to be a primary cost driver in total joint arthroplasty, and an accurate preoperative risk calculator for shoulder arthroplasty would not only help counsel patients in clinic during shared decision-making conversations but also identify high-risk individuals who may benefit from preoperative optimization and discharge planning. METHODS: Anatomic and reverse total shoulder arthroplasty cohorts from 2 geographically diverse, high-volume centers were reviewed, including 1773 cases from institution 1 (56% anatomic) and 3637 from institution 2 (50% anatomic). The predictive ability of a variety of candidate variables for discharge to SNF/rehabilitation was tested, including case type, sociodemographic factors, and the 30 Elixhauser comorbidities. Variables surviving parameter selection were incorporated into a multivariable logistic regression model built from institution 1's cohort, with accuracy then validated using institution 2's cohort. RESULTS: A total of 485 (9%) shoulder arthroplasties overall were discharged to post-acute care (anatomic: 6%, reverse: 14%, P < .0001), and these patients had significantly higher rates of unplanned 90-day readmission (5% vs. 3%, P = .0492). Cases performed for preoperative fracture were more likely to require post-acute care (13% vs. 3%, P < .0001), whereas revision cases were not (10% vs. 10%, P = .8015). A multivariable logistic regression model derived from the institution 1 cohort demonstrated excellent preliminary accuracy (area under the receiver operating characteristic curve [AUC]: 0.87), requiring only 11 preoperative variables (in order of importance): age, marital status, fracture, neurologic disease, paralysis, American Society of Anesthesiologists physical status, gender, electrolyte disorder, chronic pulmonary disease, diabetes, and coagulation deficiency. This model performed exceptionally well during external validation using the institution 2 cohort (AUC: 0.84), and to facilitate convenient use was incorporated into a freely available, online prediction tool. A model built using the combined cohort demonstrated even higher accuracy (AUC: 0.89). CONCLUSIONS: This validated preoperative clinical decision tool reaches excellent predictive accuracy for discharge to SNF/rehabilitation following shoulder arthroplasty, providing a vital tool for both patient counseling and preoperative discharge planning. Further, model parameters should form the basis for reimbursement legislation adjusting for patient comorbidities, ensuring no disparities in access arise for at-risk populations.

Cardiovascular findings on chest computed tomography associated with COVID-19 adverse clinical outcomes.

STUDY OBJECTIVE: Chest computed tomography (chest CT) is routinely obtained to assess disease severity in COVID-19. While pulmonary findings are well-described in COVID-19, the implications of cardiovascular findings are less well understood. We evaluated the impact of cardiovascular findings on chest CT on the adverse composite outcome (ACO) of hospitalized COVID-19 patients. SETTING/PARTICIPANTS: 245 COVID-19 patients who underwent chest CT at Rush University Health System were included. DESIGN: Cardiovascular findings, including coronary artery calcification (CAC), aortic calcification, signs of right ventricular strain [right ventricular to left ventricular diameter ratio, pulmonary artery to aorta diameter ratio, interventricular septal position, and inferior vena cava (IVC) reflux], were measured by trained physicians. INTERVENTIONS/MAIN OUTCOME MEASURES: These findings, along with pulmonary findings, were analyzed using univariable logistic analysis to determine the risk of ACO defined as intensive care admission, need for non-invasive positive pressure ventilation, intubation, in-hospital and 60-day mortality. Secondary endpoints included individual components of the ACO. RESULTS: Aortic calcification was independently associated with an increased risk of the ACO (odds ratio 1.86, 95% confidence interval (1.11-3.17) p < 0.05). Aortic calcification, CAC, abnormal septal position, or IVC reflux of contrast were all significantly associated with 60-day mortality and major adverse cardiovascular events. IVC reflux was associated with in-hospital mortality (p = 0.005). CONCLUSION: Incidental cardiovascular findings on chest CT are clinically important imaging markers in COVID-19. It is important to ascertain and routinely report cardiovascular findings on CT imaging of COVID-19 patients as they have potential to identify high risk patients.

Sex Differences in COVID-19 Hospitalization and Mortality.

Background: To investigate sex differences in coronavirus disease 2019 (COVID-19) outcomes in a large Illinois-based cohort. Methods: A multicenter retrospective cohort study compared males versus females with COVID-19 infections from March 1, 2020, to June 21, 2020, in the Rush University System. We analyzed sex differences in rates of hospitalization, intensive care unit (ICU) admission, vasopressor use, endotracheal intubation, and death in this cohort. A multivariable model correcting for age and sum of comorbidities was used to explore associations between sex and COVID-19-related outcomes. Results: There were 8108 positive COVID-19 patients-4300 (53.0%) females and 3808 (47.0%) males. Males had higher rates of hospitalization (19% vs. 13%; p < 0.001), ICU transfer (8% vs. 4%; p < 0.001), vasopressor support (4% vs. 2%; p < 0.001), and endotracheal intubation (5% vs. 2%; p < 0.001). Of those who died, 92 were males and 64 were females (2% vs. 1%; p = 0.003). A multivariable model correcting for age and sum of comorbidities showed a significant association between male sex and mortality in the total cohort (odds ratio, 1.96; 95% confidence interval, 1.34-2.90; p = 0.001). Conclusion: Male sex was independently associated with death, hospitalization, ICU admissions, and need for vasopressors or endotracheal intubation, after correction for important covariates.

The prognostic value of cardiac troponin for 60 day mortality and major adverse events in COVID-19 patients.

BACKGROUND: The variability of coronavirus disease 2019 (COVID-19) illness severity has puzzled clinicians and has sparked efforts to better predict who would benefit from rapid intervention. One promising biomarker for in-hospital morbidity and mortality is cardiac troponin (cTn). METHODS: A retrospective study of 1331 adult patients with COVID-19 admitted to the Rush University System in Illinois, USA was performed. Patients without cTn measurement during their admission or a history of end stage renal disease or stage 5 chronic kidney disease were excluded. Using logistic regression adjusted for baseline characteristics, pre-existing comorbidities, and other laboratory markers of inflammation, cTn was assessed as a predictor of 60-day mortality and severe COVID-19 infection, consisting of a composite of 60-day mortality, need for intensive care unit, or requiring non-invasive positive pressure ventilation or intubation. RESULTS: A total of 772 patients met inclusion criteria. Of these, 69 (8.9%) had mild cTn elevation (> 1 to < 2x upper limit of normal (ULN)) and 46 (6.0%) had severe cTn elevation (≥ 2x ULN). Regardless of baseline characteristics, comorbidities, and initial c-reactive protein, lactate dehydrogenase, and ferritin, when compared to the normal cTn group, mild cTn elevation and severe cTn elevation were predictors of severe COVID-19 infection (adjusted OR [aOR] aOR 3.00 [CI: 1.51 - 6.29], P < 0.01; aOR 9.96 [CI: 2.75 - 64.23], P < 0.01, respectively); severe cTn elevation was a predictor of in-hospital mortality (aOR 2.42 [CI: 1.10 - 5.21], P < 0.05) and 60-day mortality (aOR 2.45 [CI: 1.13 - 5.25], P < 0.05). CONCLUSION: In our cohort, both mild and severe initial cTn elevation were predictors of severe COVID-19 infection, while only severe cTn elevation was predictive of 60-day mortality. First cTn value on hospitalization is a valuable longitudinal prognosticator for COVID-19 disease severity and mortality.