Potentially Avoidable Surgical Intensive Care Unit Admissions and Disposition Delays

Relationship between blood urea nitrogen to serum albumin ratio and short-term mortality among patients from the surgical intensive care unit: a population-based real-world study

BACKGROUND

Patients admitted to the surgical intensive care unit (SICU) often suffer from multi-organ dysfunction and have a high mortality rate. Therefore, finding a simple but effective clinical indicator to predict the prognosis of patients is essential to improve their survival. The aim of this study was to investigate the relationship between blood urea nitrogen to serum albumin ratio (B/A) and short-term mortality among patients from the SICU.

METHODS

All eligible adult patients admitted to the SICU from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were recruited for this study. Participants were divided into a death group (n = 638) and a survival group (n = 2,048) based on the 90-day prognosis, and then grouped by B/A quartiles. We used restricted cubic splines (RCS) to visually analyze the correlation of B/A with 30- and 90-day risk of death. Cumulative survival rates were estimated using Kaplan-Meier survival curves according to B/A quartiles and evaluated using the log-rank test. Cox proportional risk models were developed and sensitivity analyses were performed to explore whether B/A was independently associated with short-term outcomes in SICU patients. Receiver operating characteristic (ROC) curves were analyzed to ascertain the value of B/A for prognosticating 90-day outcome.

RESULTS

A total of 2686 participants were included in the final study, and their 30-day and 90-day all-cause mortality rates were 17.61% and 23.75%, respectively. The differences in 30-day and 90-day mortality rates were statistically significant among the four groups of patients (all p < 0.001). RCS curves showed that B/A was linearly associated with the risk of 30-day and 90-day all-cause mortality in SICU patients (χ2 = 0.960, p = 0.811; χ2 = 1.940, p = 0.584). Kaplan-Meier analysis showed that the 90-day cumulative survival rate gradually decreased as B/A increased, with patients in the highest quartile of B/A having the lowest survival rate (p < 0.001). Cox regression indicated that elevated B/A (> 9.69) was an independent risk factor for 30-day and 90-day all-cause mortality in SICU patients. The analysis of ROC curves demonstrated that B/A exhibited a significant predictive ability for 90-day mortality, with an optimal threshold of 6.587, a sensitivity of 56.9%, and a specificity of 64.8%.

CONCLUSIONS

Elevated B/A (> 9.69) on admission was an independent risk factor for short-term mortality in SICU patients, and clinicians should pay more attention to this group of patients and intervene clinically at an early stage to reduce mortality.

Level of Care Appropriateness in VA Inpatient Surgery Cases

PURPOSE OF STUDY

Within the Veterans Health Administration, utilization management (UM) focuses on reducing unnecessary or inappropriate hospitalizations by applying evidence-based criteria to evaluate whether the patient is placed in the right level of care. This study examined inpatient surgery cases to classify reasons for not meeting criteria and to identify the appropriate level of care for admissions and subsequent bed days of care.

PRIMARY PRACTICE SETTINGS

There were 129 VA Medical Centers in which inpatient UM reviews were performed during that time, of which 109 facilities had UM reviews conducted in Surgery Service.

METHODOLOGY AND SAMPLE

All admissions to surgery service during fiscal year 2019 (October 1, 2018 to September 30, 2019) that had a UM review entered in the national database were extracted, including current level of care, recommended level of care, and reasons for not meeting criteria. The following demographic and diagnostic fields were supplemented from a national data warehouse: age, gender, marital status, race, ethnicity, and service connection status. Data were analyzed with descriptive statistics. Characteristics of patient demographics were compared using the χ2 test for categorical variables and the Student's t test.

RESULTS

A total of 363,963 reviews met conditions to be included in the study: 87,755 surgical admission reviews and 276,208 continued stay reviews. There were 71,274 admission reviews (81.22%) and 198,521 (71.87%) continued stay reviews that met the InterQual criteria. The primary reason for not meeting admission criteria was clinical variance (27.70%), followed by inappropriate level of care (26.85%). The leading reason for not meeting continued stay criteria was inappropriate level of care (27.81%), followed by clinical instability (25.67%). Of the admission reviews not meeting admission criteria, 64.89% were in the wrong level of care and 64.05% of continued stay reviews were also in the wrong level of care. Half of the admission reviews not meeting criteria had a recommended level of care as home/outpatient (43.51%), whereas nearly one-third (28.81%) continued stay reviews showed a recommended level of care of custodial care or skilled nursing.

IMPLICATIONS FOR CASE MANAGEMENT PRACTICE

This study identified system inefficiencies through admission and continued stay reviews of surgical inpatients. Patients admitted for ambulatory surgery or for preoperative testing prior to day of surgery resulted in avoidable bed days of care that may have contributed to patient flow issues and limited the available hospital beds for other patients. Through early collaboration with case management and care coordination professionals, alternatives can be explored that safely address the patient needs, such as temporary lodging options. There may be conditions or complications that can be anticipated on the basis of patient history. Proactive efforts to address these conditions may help avoid unnecessary bed days and extended lengths of stay.

Using EHR Data to Identify Patient Frailty and Risk for ICU Transfer

The predictive properties of four definitions of a frailty risk score (FRS) constructed using combinations of nursing flowsheet data, laboratory tests, and ICD-10 codes were examined for time to first intensive care unit (ICU) transfer in medical-surgical inpatients ≥50 years of age. Cox regression modeled time to first ICU transfer and Schemper-Henderson explained variance summarized predictive accuracy of FRS combinations. Modeling by age group and controlling for sex, all FRS measures significantly predicted time to first ICU transfer. Further multivariable modeling controlling for clinical characteristics substantially improved predictive accuracy. The effect of frailty on time to first ICU transfer depended on age, with highest risk in 50 to <60 years and ≥80 years age groups. Frailty prevalence ranged from 25.1% to 56.4%. Findings indicate that FRS-based frailty is a risk factor for time to first ICU transfer and should be considered in assessment and care-planning to address frailty in high-risk patients.Frailty prevalence was highest med-surg pts 60 to <70 years (56%); highest risk for time to first ICU transfer was in younger (50 to <60 years) and older (≥80 years) groups.

Reducing PICU-to-Floor Time-to-Transfer Decision in Critically Ill Bronchiolitis Patients using Quality Improvement Methodology

Supplemental Digital Content is available in the text.

Introduction:

Specific criteria for de-escalation from the PICU are often not included in viral bronchiolitis institutional pathways. Variability of transfer preferences can prolong PICU length of stay. We aimed to decrease the time from reaching floor-appropriate heated high flow nasal cannula (HHF) settings to the transfer decision by 20% through standardizing PICU-to-floor transfer assessment in a PICU bronchiolitis cohort.

Methods:

We included PICU bronchiolitis admissions from October 2019 to April 2020, who were 6-months to 2-years-old with no comorbidities nor intubation during their encounter. Our intervention bundle included introduction of transfer criteria and standardization of transfer-readiness assessment. The primary outcome was time from reaching floor-appropriate HHF settings [8 L per minutes (Lpm)] to placement of the transfer order (“time-to-transfer decision”). The secondary outcome was PICU length of stay. The main process measure was the proportion of patients transferred on ≥6 Lpm HHF. Balancing measures included Rapid Response Team activation and unplanned PICU readmission. We assessed admissions meeting inclusion criteria from December, 2018-March, 2019 for the preintervention baseline.

Results:

Special cause variation indicated improvement in our primary outcome and process measures. Comparison of baseline to postintervention revealed a reduction in median time-to-transfer decision (14.4–7.8 hours; P < 0.001) and increase in children transferred on ≥6 Lpm (51%–72%; P < 0.001). We observed no change in PICU length of stay or balancing measures.

Conclusion:

Standardizing de-escalation criteria and transfer-readiness assessment reduced the time-to-transfer decision out of the PICU and increased the proportion transferred on ≥6 Lpm HHF for children with viral bronchiolitis without increasing PICU readmissions.

Cost and clinical outcomes of postoperative intensive care unit versus general floor management in head and neck free flap reconstructive surgery patients

PURPOSE

To compare clinical, surgical, and cost outcomes in patients undergoing head and neck free-flap reconstructive surgery in the setting of postoperative intensive care unit (ICU) against general floor management.

METHODS

Retrospective analysis of head and neck free-flap reconstructive surgery patients at a single tertiary academic medical center. Clinical data was obtained from medical records. Cost data was obtained via the Mayo Clinic Rochester Cost Data Warehouse, which assigns Medicare reimbursement rates to all professional billed services.

RESULTS

A total of 502 patients were included, with 82 managed postoperatively in the ICU and 420 on the general floor. Major postoperative outcomes did not differ significantly between groups (Odds Ratio[OR] 1.54; p = 0.41). After covariate adjustments, patients managed in the ICU had a 3.29 day increased average length of hospital stay (Standard Error 0.71; p < 0.0001) and increased need for take-back surgery (OR 2.35; p = 0.02) when compared to the general floor. No significant differences were noted between groups in terms of early free-flap complications (OR 1.38;p = 0.35) or late free-flap complications (Hazard Ratio 0.81; p = 0.61). Short-term cost was $8772 higher in the ICU (range = $5640-$11,903; p < 0.01). Long-term cost did not differ significantly.

CONCLUSION

Postoperative management of head and neck oncologic free-flap patients in the ICU does not significantly improve major postoperative outcomes or free-flap complications when compared to general floor care, but does increase short-term costs. General floor management may be appropriate when cardiopulmonary compromise is not present.

A clinical nomogram predicting unplanned intensive care unit admission after hip fracture surgery

BACKGROUND

Despite the improvement of surgical procedures and perioperative management, a portion of patients were still at high risk for intensive care unit admission owing to severe morbidity after hip fracture surgeries. The purpose of this study was to analyze influencing factors and to construct a clinical nomogram to predict unscheduled intensive care unit admission among inpatients after hip fracture surgeries.

METHODS

We enrolled a total of 1,234 hip fracture patients, with 40 unplanned intensive care unit admissions, from January 2011 to December 2018. Demographics, chronic coexisting conditions at admission, laboratory tests, and surgical variables were collected and compared between intensive care unit admission and nonadmission groups using univariate analysis. The optimal lasso model was refined to the whole data set, and multivariate logistic regression was used to assign relative weights. A nomogram incorporating these predictors was constructed to visualize these predictors and their corresponding points of the risk for unplanned intensive care unit admission. The model was validated temporally using an independent data set from January 2019 to December 2019 by receiver operating characteristic area under the curve analysis.

RESULTS

In the development group, we identified age, chronic heart failure, coronary heart disease, chronic obstructive pulmonary disease, Parkinson disease, and serum albumin and creatinine concentration were associated with unscheduled intensive care unit admission using multivariate analysis. The final model had an area under the curve of 0.854 (95% confidence interval, 0.742-0.966). The median calculated odds ratio of intensive care unit admission based on the nomogram was significantly higher for patients in the intensive care unit admission group than in the non-intensive care unit admission group (65.93% vs 0.02%, P < .01). The validation group proved its high predictive power with an area under the curve of 0.96 (95% confidence interval, 0.91-0.99).

CONCLUSION

In this study, we identified several independent factors that may increase the risk for unexpected intensive care unit admission after hip fracture surgery and developed a clinical nomogram based on these variables. Preoperative evaluation using this nomogram might facilitate advanced intensive care unit resource management for high-risk patients whose conditions might easily deteriorate if not closely monitored in general wards after surgeries.

Elective infrainguinal lower extremity bypass for claudication is associated with high postoperative intensive care utilization

BACKGROUND

The overall use of intensive care units (ICUs) in the United States has been steadily increasing and is associated with tremendous health care costs. We suspect that the burden of ICU utilization after elective infrainguinal lower extremity bypass (LEB) procedures is high, despite relatively low risks of complications in the immediate postoperative period. We sought to identify the burden of ICU utilization after elective LEB in patients with claudication.

METHODS

We queried the Premier Healthcare Database for all adult patients undergoing first recorded elective infrainguinal LEB for claudication from 2009 to 2015. Baseline characteristics and ICU utilization on postoperative day 0 (POD 0) were identified for each patient using Premier room and board chargemaster codes. A bivariate logistic regression was performed and postestimation concordance statistics were calculated to identify predictors of postoperative ICU vs regular surgical floor admission immediately after surgery.

RESULTS

There were 6010 patients who met the selection criteria, of whom 2772 (46.1%) were admitted to the ICU and 3238 (53.9%) to the regular surgical floor on POD 0. Whereas patient-level factors were responsible for minor differences found in postoperative admission to the ICU after elective LEB, hospital characteristics made up the majority of variation in admission practices. Specifically, patients undergoing elective infrainguinal LEB in rural, nonteaching, small hospitals and those in certain geographic regions were more likely to be admitted to the ICU than to the floor (all, P < .001). Patient-level factors were poorly predictive of admission to the ICU immediately postoperatively, with C statistics ranging from 0.50 to 0.53. In contrast, hospital-level factors had higher C statistics ranging from 0.51 to 0.66, with geographic location being the strongest predictor of post-LEB ICU admission. There were no significant differences in the incidence of postoperative wound complications, major adverse limb events, major adverse cardiac events, or in-hospital mortality between groups (all, P ≥ .32). The median total hospital cost was $2340 higher for ICU compared with floor admission ($13,273 [interquartile range, $10,136-$17,883] vs $10,927 [interquartile range, $8342-$14,523]; P < .001).

CONCLUSIONS

Nearly half of patients are admitted to an ICU directly after elective infrainguinal LEB for claudication. This practice is associated with significantly higher hospital cost and is predominantly influenced by hospital-level rather than by patient-level factors. Perioperative morbidity and mortality were similar regardless of postoperative disposition. To minimize ICU utilization, postoperative care intensity should be determined by clinical severity of the patient rather than by hospital routine.

[Overtreatment in intensive care medicine]

Overtreatment, which is therapy that is neither indicated nor desired by the patient ("non-beneficial"), presents an inherent and huge problem of modern medicine and intensive care medicine in particular. Overtreatment concerns all aspects of intensive care medicine, may start already before admission at the emergency scene, the inappropriate admission to the intensive care unit, overuse in diagnostics and especially in blood sampling, in invasive procedures and in organ support therapies. It manifests itself as "too much" in sedation, relaxation, volume therapy, hemodynamic support, blood products, antibiotics and other drugs and nutrition. Most importantly, overtreatment concerns the care of the patients at the end of life when a causal therapy is no longer available. Overtreatment also has important ethical implications and violates the four fundamental principles of medical ethics. It disregards the autonomy, dignity and integrity of the patient, is by definition nonbeneficial and increases pain, suffering, prolongs dying, increases sorrow of relatives, imposes frustration for the caregivers, disregards distributive justice and harms society in general by wasting principally limited resources. Overtreatment has also become an important legal issue and because of imposing inappropriate suffering may lead to prosecution. Overtreatment is poor medicine, is no trivial offence, all must continuously work together to reduce or avoid overtreatment.

Perspectives on strained intensive care unit capacity: A survey of critical care professionals

Background

Strained intensive care unit (ICU) capacity represents a supply-demand mismatch in ICU care. Limited data have explored health care worker (HCW) perceptions of strain.

Methods

Cross-sectional survey of HCW across 16 Alberta ICUs. A web-based questionnaire captured data on demographics, strain definition, and sources, impact and strategies for management.

Results

658 HCW responded (33%; 95%CI, 32–36%), of which 452 were nurses (69%), 128 allied health (19%), 45 physicians (7%) and 33 administrators (5%). Participants (agreed/strongly agreed: 94%) reported that strain was best defined as “a time-varying imbalance between the supply of available beds, staff and/or resources and the demand to provide high-quality care for patients who may become or who are critically ill”; while some recommended defining “high-quality care”, integrating “safety”, and families in the definition. Participants reported significant contributors to strain were: “inability to discharge ICU patients due to lack of available ward beds” (97%); “increases in the volume” (89%); and “acuity and complexity of patients requiring ICU support” (88%). Strain was perceived to “increase stress levels in health care providers” (98%); and “burnout in health care providers” (96%). The highest ranked strategies were: “have more consistent and better goals-of-care conversations with patients/families outside of ICU” (95%); and “increase non-acute care beds” (92%).

Interpretation

Strain is perceived as common. HCW believe precipitants represent a mix of patient-related and operational factors. Strain is thought to have negative implications for quality of care, HCW well-being and workplace environment. Most indicated strategies “outside” of ICU settings were priorities for managing strain.

Preoperative risk factors for unexpected postoperative intensive care unit admission: A retrospective case analysis

INTRODUCTION

The purpose of this retrospective case-control study was to investigate preoperative risk factors for unexpected postoperative intensive care unit (ICU) admissions in patients undergoing non-emergent surgical procedures in a tertiary medical centre.

METHODS

A medical record review of adult patients undergoing elective non-cardiac and non-transplant major surgical procedures during the period of January 2011 through December 2015 in the operating rooms of a large university hospital was carried out. The primary outcome assessed was unexpected ICU admission, with mortality as a secondary outcome. Demographic data, length of hospital and ICU stay and preoperative comorbidities were also obtained as exposure variables. Propensity score matching was then employed to yield a study and control group.

RESULTS

The group of patients who met inclusion criteria in the study and the control group that did not require ICU admission were obtained, each containing 1191 patients after propensity matching. Patients with acute and/or chronic kidney injury (odds ratio (OR) 2.20 [1.75-2.76]), valvular heart disease (OR: 1.94 [1.33-2.85]), peripheral vascular disease (PVD) (OR: 1.41 [1.02-1.94]) and congestive heart failure (CHF) (OR: 1.80 [1.31-2.46]) were all associated with increased unexpected ICU admission. History of cerebrovascular accident (CVA) (OR: 3.03 [1.31-7.01]) and acute and/or chronic kidney injury (OR: 1.62 [1.12-2.35]) were associated with increased mortality in all patients; CVA was also associated with increased mortality (OR: 3.15 [1.21-8.20]) specifically in the ICU population.

CONCLUSIONS

CHF, acute/chronic kidney injury, PVD and valve disease were significantly associated with increased unexpected ICU admission; patients with CVA suffered increased mortality when admitted to the ICU.