The Impact of Adding a 2-Way Video Monitoring System on Falls and Costs for High-Risk Inpatients.

OBJECTIVES: We aimed to investigate the value of adding a video monitoring (VM) system with falls and costs for patients at high risk. METHODS: We conducted a retrospective, historically controlled study of adults (≥18 y old) at high risk of fall admitted at the University of Miami Hospital and Clinics from January 1 to November 30, 2020 (pre-VM) and January 1 to November 30, 2021 (post-VM); in-person sitters were available in both periods. Fall risk assessment was conducted on admission and at every nursing shift; we defined patients as high risk if their Morse Fall Scale was ≥60. We conducted a multivariable logistic regression model to evaluate the association of period (pre- versus post-VM) with falls and performed a cost analysis. RESULTS: Our primary cohort consisted of 9,034 patients at high risk of falls, 4,207 (46.6%) in the pre-VM and 4,827 (53.4%) in the post-VM period. Fall rates were higher in the pre- than the post-VM periods (3.5% versus 2.7%, P = 0.043). After adjustment, being admitted during the post-VM period was associated with a lower odds of fall (odds ratio [95% confidence interval], 0.49 [0.37-0.64], P < 0.001). The median adjusted hospital cost (in 2020 dollars) was $1,969 more for patients who fell than for patients who did not (interquartile range, $880-$2,273). Considering start-up and ongoing costs, we estimate VM implementation to partly replace in-person monitoring has potential annual cost savings of >$800,000 for a hospital similar to ours. CONCLUSIONS: Video monitoring to augment in-person sitters is an effective fall prevention initiative for patients at high risk of falls, which is likely also cost-effective.

Persistent colonization of Candida auris among inpatients rescreened as part of a weekly surveillance program.

We established a surveillance program to evaluate persistence of C. auris colonization among hospitalized patients. Overall, 17 patients (34%) had ≥1 negative result followed by a positive test, and 7 (41%) of these patients had ≥2 consecutive negative tests.

Operational impact of decreased turnaround times for Candida auris screening tests in a tertiary academic medical center.

Objective: Assess turnaround time (TAT) and cost-benefit of on-site C. auris screening and its impact on length of stay (LOS) and costs compared to reference laboratories. Design: Before-and-after retrospective cohort study. Setting: Large-tertiary medical center. Methods: We validated an on-site polymerase chain reaction-based testing platform for C. auris and retrospectively reviewed hospitalized adults who screened negative before and after platform implementation. We constructed multivariable models to assess the association of screening negative with hospital LOS/cost in the pre and postimplementation periods. We adjusted for confounders such as demographics and indwelling device use, and compared TATs for all samples tested. Results: The sensitivity and specificity of the testing platform were 100% and 98.11%, respectively, compared to send-out testing. The clinical cohort included 287 adults in the pre and 1,266 postimplementation period. The TAT was reduced by more than 2 days (3 (interquartile range (IQR): 2.0, 7.0) vs 0.42 (IQR: 0.24, 0.81), p < 0.001). Median LOS was significantly lower in the postimplementation period; however, this was no longer evident after adjustment. In relation to total cost, the time period had an effect of $6,965 (95% CI: -$481, $14,412); p = 0.067) on reducing the cost. The median adjusted total cost per patient was $7,045 (IQR: $3,805, $13,924) less in the post vs the preimplementation period. Conclusions: Our assessment did not find a statistically significant change in LOS, nevertheless, on-site testing was not cost-prohibitive for the institution. The value of on-site testing may be supported if an institutional C. auris reduction strategy emphasizes faster TATs.

Association of language concordance and restraint use in adults receiving mechanical ventilation.

PURPOSE: Clinician-patient language concordance improves patient outcomes in non-intensive care unit (ICU) settings. We sought to assess the association of ICU nurse-patient language concordance with delirium-related outcomes. METHODS: We conducted a retrospective cohort study of adult English- or Spanish-speaking mechanically ventilated ICU patients admitted to ICUs at the University of Miami Hospital and Clinics (January 2021-September 2022). Our primary exposure was nurse-patient language concordance on each shift. We used mixed-effects multivariable regression to evaluate the association of language concordance with the primary outcome of restraint use, and secondary outcomes of agitation and identification of delirium, during each shift (with patient as a random effect). RESULTS: Our cohort included 4326 shifts (3380 [78.1%] with language concordance) from 548 patients and 157 nurses. Spanish language was preferred by 269 (49.1%) of patients. English-speaking patients tended to be younger (65 [53, 75] vs 73 [61, 83], p < 0.001) and of non-Hispanic ethnicity (55.5% vs 7.1%, p < 0.001). English-speakers had restraints ordered on fewer of their included shifts (0 [0, 3] vs 1 [0, 3], p = 0.005). After adjustment, the odds of restraint use on shifts with language concordance was significantly lower (odds ratio [OR, 95% confidence interval [CI]]: 0.50 [0.39-0.63], p < 0.001). Agitation (18.6% vs 25.2%; OR [95% CI]: 0.71 [0.55-0.92], p = 0.009) and delirium identification (34.5% vs 41.3%; OR [95% CI]: 0.54 [0.34-0.88], p = 0.014) were also less common. CONCLUSIONS: We identified a twofold reduction in the odds of restraint use among mechanically ventilated patients for language concordant nurse-patient dyads. Ensuring nurse-patient language concordance may improve ICU delirium, agitation, and restraint use.

Postcardiac Surgery Euglycemic Diabetic Ketoacidosis in Patients on Sodium-Glucose Cotransporter 2 Inhibitors.

OBJECTIVES: To evaluate sodium-glucose cotransporter 2 inhibitors (SGLT2i) use and complications (euglycemic diabetic ketoacidosis [eDKA] rate, mortality, infection, hospital, and cardiovascular intensive care unit [CVICU] length of stay [LOS]) in patients undergoing cardiac surgery. DESIGN: A retrospective study. SETTING: At an academic university hospital. PARTICIPANTS: Adult patients undergoing cardiac surgery. INTERVENTIONS: SGLT2i use versus no SGLT2i use. MEASUREMENTS AND MAIN RESULTS: The authors evaluated patients undergoing cardiac surgery within 24 hours of hospital admission (between February 2, 2019 to May 26, 2022) for SGLT2i prevalence and eDKA frequency. The outcomes were compared using Wilcoxon rank sum and chi-square testing as appropriate. The cohort included 1,654 patients undergoing cardiac surgery, of whom 53 (3.2%) were prescribed an SGLT2i before surgery; 8 (15.1%) of 53 had eDKA. The authors found no differences between patients with and without SGLT2i use in hospital LOS (median [IQR]: 4.5 [3.5-6.3] v 4.4 [3.4-5.6] days, p = 0.46) or CVICU LOS (median [IQR]: 1.2 [1.0-2.2] v 1.1 [1.0-1.9] days, p = 0.22), 30-day mortality (1.9% v 0.7%, p = 0.31), or sternal infections (0.0% v 0.3%, p = 0.69). Among patients prescribed an SGLT2i, those with and without eDKA had similar hospital LOS (5.1 [4.0-5.8] v 4.4 [3.4-6.3], p = 0.76); however, CVICU LOS was longer in patients with eDKA (2.2 [1.5-2.9] v 1.2 [0.9-2.0], p = 0.042). Mortality (0.0% v 2.2%, p = 0.67) and wound infections (0.0% v 0.0%, p > 0.99) were similarly rare. CONCLUSIONS: Postoperative eDKA occurred in 15% of patients on an SGLT2i prior to cardiac surgery, and was associated with longer CVICU LOS. Future studies into SGLT2i management perioperatively are important.

The clinical effectiveness of REGEN-COV in SARS-CoV-2 infection with Omicron versus Delta variants.

BACKGROUND: In vitro studies suggesting that REGEN-COV (casirivimab plus imdevimab monoclonal antibodies) had poor efficacy against Omicron-variant SARS-CoV-2 infection led to amendment of REGEN-COV's Emergency Use Authorization to recommend use only in regions without high Omicron prevalence. REGEN-COV's relative clinical effectiveness for Omicron is unknown. METHODS AND FINDINGS: We conducted a retrospective cohort study of non-hospitalized adults who tested positive for SARS-CoV-2 by polymerase chain reaction at the University of Miami Health System from July 19 -November 21, 2021 (Delta period) and December 6, 2021 -January 7, 2022 (Omicron period). Subjects were stratified be REGEN-COV receipt within 72h of test positivity and by time period of infection. We constructed multivariable logistic regression models to assess the differential association of REGEN-COV receipt with hospitalization within 30 days (primary outcome) and ED presentation; all models included three exposure terms (REGEN-COV receipt, Omicron vs Delta period, interaction of REGEN-COV with time period) and potential confounders (vaccination status, vaccine boosting, cancer diagnosis). Our cohort consisted of 2,083 adults in the Delta period (213 [10.2%] received REGEN-COV) and 4,201 in the Omicron period (156 [3.7%] received REGEN-COV). Hospitalization was less common during the Omicron period than during Delta (0.9% vs 1.7%, p = 0.78) and more common for patients receiving REGEN-COV than not (5.7% vs 0.9%, p<0.001). After adjustment, we found no differential association of REGEN-COV use during Omicron vs Delta with hospitalization within 30d (adjusted odds ratio [95% confidence interval] for the interaction term: 2.31 [0.76-6.92], p = 0.13). Similarly, we found no differential association for hospitalization within 15d (2.45 [0.63-9.59], p = 0.20) or emergency department presentation within 30d (1.43 [0.57-3.51], p = 0.40) or within 15d (1.79 [0.65-4.82], p = 0.30). CONCLUSIONS: Within the limitations of this study's power to detect a difference, we identified no differential effectiveness of REGEN-COV in the context of Omicron vs Delta SARS-CoV-2 infection.

Predictive Value of Sequential Organ Failure Assessment Score across Patients with and without COVID-19 Infection.

Rationale: Sequential organ failure assessment (SOFA) scores are commonly used in crisis standards of care policies to assist in resource allocation. The relative predictive value of SOFA by coronavirus disease (COVID-19) infection status and among racial and ethnic subgroups within patients infected with COVID-19 is unknown. Objectives: To evaluate the accuracy and calibration of SOFA in predicting hospital mortality by COVID-19 infection status and across racial and ethnic subgroups. Methods: We performed a retrospective cohort study of adult admissions to the University of Miami Hospital and Clinics inpatient wards (July 1, 2020-April 1, 2021). We primarily considered maximum SOFA within 48 hours of hospitalization. We assessed accuracy using the area under the receiver operating characteristic curve (AUROC) and created calibration belts. Considered subgroups were defined by COVID-19 infection status (by severe acute respiratory syndrome coronavirus 2 polymerase chain reaction testing) and prevalent racial and ethnic minorities. Comparisons across subgroups were made with DeLong testing for discriminative accuracy and visualization of calibration belts. Results: Our primary cohort consisted of 20,045 hospitalizations, of which 1,894 (9.5%) were COVID-19 positive. SOFA was similarly accurate for COVID-19-positive (AUROC, 0.835) and COVID-19-negative (AUROC, 0.810; P = 0.15) admissions but was slightly better calibrated in patients who were positive for COVID-19. For those with critical illness, maximum SOFA score accuracy at critical illness onset also did not differ by COVID-19 status (AUROC, COVID-19 positive vs. negative: intensive care unit admissions, 0.751 vs. 0.775; P = 0.46; mechanically ventilated, 0.713 vs. 0.792, P = 0.13), and calibration was again better for patients positive for COVID-19. Among patients with COVID-19, SOFA accuracy was similar between the non-Hispanic White population (AUROC, 0.894) and racial and ethnic minorities (Hispanic White population: AUROC, 0.824 [P vs. non-Hispanic White = 0.05]; non-Hispanic Black population: AUROC, 0.800 [P = 0.12]; Hispanic Black population: AUROC, 0.948 [P = 0.31]). This similar accuracy was also found for those without COVID-19 (non-Hispanic White population: AUROC, 0.829; Hispanic White population: AUROC, 0.811 [P = 0.37]; Hispanic Black population: AUROC, 0.828 [P = 0.97]; non-Hispanic Black population: AUROC, 0.867 [P = 0.46]). SOFA was well calibrated for all racial and ethnic groups with COVID-19 but estimated mortality more variably and performed less well across races and ethnicities without COVID-19. Conclusions: SOFA accuracy does not differ by COVID-19 status and is similar among racial and ethnic groups both with and without COVID-19. Calibration is better for COVID-19-infected patients and, among those without COVID-19, varies by race and ethnicity.

Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic.

OBJECTIVES: We describe a hospital's implementation of predictive models to optimise emergency response to the COVID-19 pandemic. METHODS: We were tasked to construct and evaluate COVID-19 driven predictive models to identify possible planning and resource utilisation scenarios. We used system dynamics to derive a series of chain susceptible, infected and recovered (SIR) models. We then built a discrete event simulation using the system dynamics output and bootstrapped electronic medical record data to approximate the weekly effect of tuning surgical volume on hospital census. We evaluated performance via a model fit assessment and cross-model comparison. RESULTS: We outlined the design and implementation of predictive models to support management decision making around areas impacted by COVID-19. The fit assessments indicated the models were most useful after 30 days from onset of local cases. We found our subreports were most accurate up to 7 days after model run.DiscusssionOur model allowed us to shape our health system's executive policy response to implement a 'hospital within a hospital'-one for patients with COVID-19 within a hospital able to care for the regular non-COVID-19 population. The surgical scheduleis modified according to models that predict the number of new patients withCovid-19 who require admission. This enabled our hospital to coordinateresources to continue to support the community at large. Challenges includedthe need to frequently adjust or create new models to meet rapidly evolvingrequirements, communication, and adoption, and to coordinate the needs ofmultiple stakeholders. The model we created can be adapted to other health systems,provide a mechanism to predict local peaks in cases and inform hospitalleadership regarding bed allocation, surgical volumes, staffing, and suppliesone for COVID-19 patients within a hospital able to care for the regularnon-COVID-19 population. CONCLUSION: Predictive models are essential tools in supporting decision making when coordinating clinical operations during a pandemic.

Association of Race and Ethnicity with COVID-19 Test Positivity and Hospitalization Is Mediated by Socioeconomic Factors.

Rationale: Black race and Hispanic ethnicity are associated with increased risks for coronavirus disease (COVID-19) infection and severity. It is purported that socioeconomic factors may drive this association, but data supporting this assertion are sparse. Objectives: To evaluate whether socioeconomic factors mediate the association of race/ethnicity with COVID-19 incidence and outcomes. Methods: We conducted a retrospective cohort study of adults tested for (cohort 1) or hospitalized with (cohort 2) COVID-19 between March 1, 2020, and July 23, 2020, at the University of Miami Hospital and Clinics. Our primary exposure was race/ethnicity. We considered socioeconomic factors as potential mediators of our exposure's association with outcomes. We used standard statistics to describe our cohorts and multivariable regression modeling to identify associations of race/ethnicity with our primary outcomes, one for each cohort, of test positivity (cohort 1) and hospital mortality (cohort 2). We performed a mediation analysis to see whether household income, population density, and household size mediated the association of race/ethnicity with outcomes. Results: Our cohorts included 15,473 patients tested (29.0% non-Hispanic White, 48.1% Hispanic White, 15.0% non-Hispanic Black, 1.7% Hispanic Black, and 1.6% other) and 295 patients hospitalized (9.2% non-Hispanic White, 56.9% Hispanic White, 21.4% non-Hispanic Black, 2.4% Hispanic Black, and 10.2% other). Among those tested, 1,256 patients (8.1%) tested positive, and, of the hospitalized patients, 47 (15.9%) died. After adjustment for demographics, race/ethnicity was associated with test positivity-odds-ratio (95% confidence interval [CI]) versus non-Hispanic White for Non-Hispanic Black: 3.21 (2.60-3.96), Hispanic White: 2.72 (2.28-3.26), and Hispanic Black: 3.55 (2.33-5.28). Population density mediated this association (percentage mediated, 17%; 95% CI, 11-31%), as did median income (27%; 95% CI, 18-52%) and household size (20%; 95% CI, 12-45%). There was no association between race/ethnicity and mortality, although this analysis was underpowered. Conclusions: Black race and Hispanic ethnicity are associated with an increased odds of COVID-19 positivity. This association is substantially mediated by socioeconomic factors.