MRI in Acutely Ill Medical Patients in an Irish Hospital: Influence on Outcomes and Length of Hospital Stay

Towards 24/7 MRI: the effect of routine weekend inpatient MRI scanning on patient waiting times

BACKGROUND

Demand for inpatient MRI outstrips capacity which results in long waiting lists. The hospital commenced a routine weekend MRI service in January 2023.

AIM

The aim of this study was to investigate the effect of a limited routine weekend MRI service on MRI turnaround times.

METHODS

Waiting times for inpatient MRI scans performed before and after the introduction of weekend MRI from January 1 to August 31, 2022, and January 1 to August 31, 2023, were obtained. The turnaround time (TAT) and request category for each study were calculated. Category 1 requests were required immediately, category 2 requests were urgent and category 3 requests were routine.

RESULTS

There was a 6% (n = 128) increase in MRI inpatient scanning activity in 2023 (n = 2449) compared to 2022 (n = 2322). There was a significant improvement in overall mean TAT for inpatient MRIs (p < .001) in 2023 (mean 65.2 h, range 0-555 h) compared to 2022 (mean 98.3 h, range 0-816 h). There was no significant difference in the mean waiting time for category 1 MRIs between 2022 and 2023. There was a significant improvement (p < .001) in mean waiting time in 2023 (mean 37.2 h, range 0-555) compared to 2022 (mean 55.4 h, range 0-816) for category 2 MRI. The mean waiting time for category 3 studies also significantly improved (p < .001) in 2023 (mean 93.4 h, range 1-2663) when compared to 2022 (mean 154.8, range 1-1706).

CONCLUSION

Routine weekend inpatient MRI significantly shortens inpatient waiting times.

Imaging delays among medical inpatients in Toronto, Ontario: A cohort study

BACKGROUND

Imaging procedures are commonly performed on hospitalized patients and waiting for these could increase length-of-stay. The study objective was to quantify delays for imaging procedures in General Internal Medicine and identify contributing patient, physician, and system factors.

METHODS

This was a retrospective cohort study of medical inpatients admitted to 5 hospitals in Toronto, Ontario (2010-2019), with at least one imaging procedure (CT, MRI, ultrasound, or peripherally-inserted central catheter [PICC] insertion). The primary outcome was time-to-test, and the secondary outcome was acute length-of-stay after test ordering.

RESULTS

The study cohort included 73,107 hospitalizations. Time-to-test was longest for MRI (median 22 hours) and shortest for CT (median 7 hours). The greatest contributors to time-to-test were system factors such as hospital site (up to 22 additional hours), location of test ordering (up to 10 additional hours), the timing of test ordering relative to admission (up to 13 additional hours), and ordering during weekends (up to 21 additional hours). Older patient age, having more comorbidities, and residence in a low-income neighborhood were also associated with testing delays. Each additional hour spent waiting for a test was associated with increased acute length-of-stay after test ordering, ranging from 0.4 additional hours for CT to 1.2 hours for MRI.

CONCLUSIONS

The greatest contributors to testing delays relate to when and where a test was ordered. Wait times affect length-of-stay and the quality of patient care. Hospitals can apply our novel approach to explore opportunities to decrease testing delays locally.

Patient-level factors influencing adherence to follow-up imaging recommendations

PURPOSE

To determine which, if any, patient-level factors were associated with differences in completion of follow-up imaging recommendations at a tertiary academic medical center.

METHODS

In this IRB-approved, retrospective cohort study, approximately one month of imaging recommendations were reviewed from 2017 at a single academic institution that contained key words recommending follow-up imaging. Age, gender, race/ethnicity, insurance, smoking history, primary language, BMI, and home address were recorded via chart extraction. Home addresses were geocoded to Census Block Groups and assigned to a quintile of neighborhood socioeconomic status. A multivariate logistic regression model was used to evaluate each predictor variable with significance set to p = 0.05.

RESULTS

A total of 13,421 imaging reports that included additional follow-up recommendations were identified. Of the 1013 included reports that recommended follow-up, 350 recommended additional imaging and were analyzed. Three hundred eight (88.00%) had corresponding follow-up imaging present and the insurance payor was known for 266 (86.36%) patients: 146 (47.40%) had commercial insurance, 35 (11.36%) had Medicaid, and 85 (27.60%) had Medicare. Patients with Medicaid had over four times lower odds of completing follow-up imaging compared to patients with commercial insurance (OR 0.24, 95% CI 0.06-0.88, p = 0.032). Age, gender, race/ethnicity, smoking history, primary language, BMI, and neighborhood socioeconomic status were not independently associated with differences in follow-up imaging completion.

CONCLUSION

Patients with Medicaid had decreased odds of completing follow-up imaging recommendations compared to patients with commercial insurance.

Secondary review reduced inpatient MRI orders and avoidable hospital days

INTRODUCTION

Medical centers have dramatically increased the use of magnetic resonance imaging (MRI). At 2 large academic tertiary care centers in New York City, nearly half of inpatient MRI orders took more than 12 h to complete, delaying patient discharge and increasing avoidable hospital days. We posited that transitioning inpatient MRIs to outpatient facilities, when safe and appropriate, could reduce inpatient MRI orders and avoidable hospital days.

METHODS

We manually reviewed 59 inpatient MRI orders delayed on the estimated date of discharge (EDD). These orders were often delayed due to no standard process to escalate orders for medical reasons or no system to coordinate outpatient orders. We developed a revised workflow involving an automation platform that flagged inpatient MRI orders requested within 24 h of the EDD and emailed the care team to request a second review of the order. The care team reconsidered whether the order was (1) required for discharge, (2) non-urgent and could be converted to an outpatient order, or (3) unnecessary and could be canceled.

RESULTS

Over 9 months, the automation platform flagged 618 inpatient MRI orders, of which 53.9% (333/618) were reviewed by the care team. Among the orders, 24.0% (80/333) of reviewed orders and 12.9% (80/618) of all orders were transitioned to either outpatient or canceled orders. These transitioned orders were associated with 267 fewer avoidable hospital days and a cost savings of $199,194.

CONCLUSION

A standardized process and second review of inpatient MRI orders on the EDD can reduce inappropriate orders and more effectively use inpatient imaging resources.

PRECIS

A standardized workflow and automation platform encouraged a second review of inpatient MRI orders to reduce inappropriate orders, avoidable hospital days, and hospital costs.

Elimination of Contrast Agent Gadobutrol with Sustained Low Efficiency Daily Dialysis Compared to Intermittent Hemodialysis

BACKGROUND

In patients with renal failure, gadolinium-based contrast agents (GBCA) can be removed by intermittent hemodialysis (iHD) to prevent possible toxic effects. There is no data on the efficacy of GBCA removal via sustained low efficiency daily dialysis (SLEDD) which is mainly used in intensive care unit (ICU) patients.

METHODS

We compared the elimination of the GBCA gadobutrol in 6 ICU patients treated with SLEDD (6-12 h, 90 L dialysate) with 7 normal ward inpatients treated with iHD (4 h, dialysate flow 500 mL/min). Both groups received 3 dialysis sessions on 3 consecutive days starting after the application of gadobutrol. Blood samples were drawn before and after each session and total dialysate, as well as urine was collected. Gadolinium (Gd) concentrations were measured using mass spectrometry and eliminated Gd was calculated from dialysate and urine.

RESULTS

The initial mean plasma Gd concentration was 385 ± 183 µM for the iHD and 270 ± 97 µM for the SLEDD group, respectively (p > 0.05). The Gd-reduction rate after the first dialysis session was 83 ± 9 and 67 ± 9% for the iHD and the SLEDD groups, respectively (p = 0.0083). The Gd-reduction rate after the second and third dialysis was 94-98 and 89-96% for the iHD and the SLEDD groups (p > 0.05). The total eliminated Gd was 89 ± 14 and 91 ± 4% of the dose in the iHD and the SLEDD groups, respectively (p > 0.05). Gd dialyzer clearance was 95 ± 22 mL/min and 79 ± 19 mL/min for iHD and SLEDD, respectively (p > 0.05).

CONCLUSIONS

Gd-elimination with SLEDD is equally effective as iHD and can be safely used to remove GBCA in ICU patients.

Radiology imaging delays as independent predictors of length of hospital stay for emergency medical admissions

AIM

To investigate the extent to which the time to completion for computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound could be shown to influence the length of stay and costs incurred while in hospital, while accounting for patient acuity.

MATERIALS AND METHODS

All emergency admissions, totalling 25,326 imaging investigations between 2010-2014 were evaluated. The 50(th), 75(th), and 90(th) centiles of completion times for each imaging type was entered into a multivariable truncated Poisson regression model predicting the length of hospital stay. Estimates of risk (odds or incidence rate ratios [IRRs]) of the regressors were adjusted for acute illness severity, Charlson comorbidity index, chronic disabling disease score, and sepsis status. Quantile regression analysis was used to examine the impact of imaging on total hospital costs.

RESULTS

For all imaging examinations, longer hospital lengths of stay were shown to be related to delays in imaging time. Increased delays in CT and MRI were shown to be associated with increased hospital episode costs, while ultrasound did not independently predict increased hospital costs. The magnitude of the effect of imaging delays on episode costs were equivalent to some measures of illness severity.

CONCLUSION

CT, MRI, and ultrasound are undertaken in patients with differing clinical complexity; however, even with adjustment for complexity, the time delay in a more expeditious radiological service could potentially shorten the hospital episode and reduce costs.

Pattern of Investigation Reflects Risk Profile in Emergency Medical Admissions

Demand for hospital resources may increase over time; we have examined all emergency admissions (51,136 episodes) from 2005 to 2013 for underlying trends and whether resource utilization and clinical risk are correlated. We used logistic regression of the resource indicator against 30-day in-hospital mortality and adjusted this risk estimate for other outcome predictors. Generally, resource indicators predicted an increased risk of a 30-day in-hospital death. For CT Brain the Odds Ratio (OR) was 1.37 (95% CI: 1.27, 1.50), CT Abdomen 3.48 (95% CI: 3.02, 4.02) and CT Chest, Thorax, Abdomen and Pelvis 2.50 (95% CI: 2.10, 2.97). Services allied to medicine including Physiotherapy 2.57 (95% CI: 2.35, 2.81), Dietetics 2.53 (95% CI: 2.27, 2.82), Speech and Language 5.29 (95% CI: 4.57, 6.05), Occupational Therapy 2.65 (95% CI: 2.38, 2.94) and Social Work 1.65 (95% CI: 1.48, 1.83) all predicted an increased risk. The in-hospital 30-day mortality increased with resource utilization, from 4.7% (none) to 27.0% (five resources). In acute medical illness, the use of radiological investigations and allied professionals increased over time. Resource utilization was calibrated from case complexity/30-day in-hospital mortality suggesting that complexity determined the need for and validated the use of these resources.

Factors associated with length of stay following an emergency medical admission

BACKGROUND

Hospitals are under pressure to use resources in the most efficient manner. We have examined the factors predicting Length of Stay (LOS) in one institution, using a database of all episodes of emergency medical admissions prospectively collected over 12 years.

AIM

To examine the ability to predict hospital LOS following an emergency medical hospital admission.

METHODS

All emergency admissions (66,933 episodes; 36,271 patients) to St. James's Hospital, Dublin, Ireland over a 12-year period (2002-2013) were evaluated in relation to LOS. Predictor variables (identified univariately) were entered into a multiple logistic regression model to predict a longer or shorter LOS (bivariate at the median). The data was also modelled as count data (absolute LOS), using zero truncated Poisson regression methodology. Appropriate post-estimation techniques for model fit were then applied to assess the resulting model.

RESULTS

The major predictors of LOS included Acute Illness Severity (biochemical laboratory score at admission), Charlson co-morbidity, Manchester Triage Category at admission, Diagnosis Related Group, sepsis status (based on blood culture result), and Chronic Disease Score Indicator. The full model to predict a LOS above or below the median had an Area Under Receiver Operating Characteristic (AUROC) of 0.71 (95% CI: 0.70, 0.71). The truncated Poisson model appeared to achieve a good model fit (R(2) statistic=0.76).

CONCLUSION

Predictor variables strongly correlated with LOS; there were linear increases within categories and summation between variables. More predictor variables may improve model reliability but predicting LOS ranges or quantiles may be more realistic, based on these results.