Assessing the utility of ICU readmissions as a quality metric: an analysis of changes mediated by residency work-hour reforms

Development of a Simple Scoring System for Predicting Discharge Safety from the Medical ICU to Low-Acuity Wards: The Role of the Sequential Organ Failure Assessment Score, Albumin, and Red Blood Cell Distribution Width

Despite advancements in artificial intelligence-based decision-making, transitioning patients from intensive care units (ICUs) to low-acuity wards is challenging, especially in resource-limited settings. This study aimed to develop a simple scoring system to predict ICU discharge safety. We retrospectively analyzed patients admitted to a tertiary hospital's medical ICU (MICU) between July 2016 and December 2021. This period was divided into two phases for model development and validation. We identified risk factors associated with unexpected death within 14 days of MICU discharge and developed a predictive scoring system that incorporated these factors. We verified the system's performance using validation data. In the development cohort, 522 patients were discharged from the MICU, and 42 (8.04%) died unexpectedly. In multivariate analysis, the Sequential Organ Failure Assessment (SOFA) score (odds ratio [OR] 1.26, 95% confidence interval [CI] 1.13-1.41), red blood cell distribution width (RDW) (OR 1.20, 95% CI 1.07-1.36), and albumin (OR 0.37, 95% CI 0.16-0.84) were predictors of unexpected death. Each variable was assigned a weighted point in the scoring system, and the area under the curve (AUC) was 0.788 (95% CI 0.714-0.855). The scoring system was performed using an AUC of 0.738 (95% CI 0.653-0.822) in the validation cohort of 343 patients with 9.62% of unexpected deaths. When a cut-off of 0.032 was applied, a sensitivity and a specificity of 81.8% and 55.2%, respectively, were achieved. This simple bedside predictive score for ICU discharge uses the SOFA score, albumin level, and RDW to aid in timely decision-making and optimize critical care facility allocation in resource-limited settings.

Determinants of Readmission in the Intensive Care Unit: A Prospective Observational Study

Background The antecedents of readmission among survivors of intensive care units (ICUs) are complex and comprise an array of elements that impact the rehabilitation process after leaving the ICU. The aforementioned determinants may comprise socioeconomic factors, access to follow-up healthcare, the nature and severity of the initial illness or injury, the presence of comorbidities, the sufficiency of transitional care and rehabilitation services, and patient and family support systems. Added to this, the risk of readmission may be increased by complications that develop during the ICU stay, including but not limited to infections, organ dysfunction, and psychological distress. Comprehending these determinants is of the utmost importance for healthcare providers in order to execute focused interventions that seek to diminish readmission rates, enhance patient outcomes, and elevate the standard of care for survivors of ICUs. Objective The objective of the study is to determine the factors associated with readmission among ICU survivors and the cause of readmission. Methodology This prospective observational study was conducted in a tertiary-level ICU. The duration of the study was one year and we enrolled 108 ICU survivors in our study. We have recorded patient demographic data, comorbidity, primary diagnosis, previous treatment history (vasopressor, sedation), causes of readmission, duration of previous ICU stay, and outcome of readmitted patient (discharge, death, and transfer to lower facility). Result The incidence of readmission in our ICU is 10.4%; 50-70 age groups are more prone to readmission of which the male sex is predominant (64.81%). In our study, hypertension (cardiac, 18.52%) and diabetes mellitus (11.11%) were the most common comorbidities reported in readmitted patients. The majority of patients who get readmission suffered from blunt trauma abdomen. In the majority of readmitted patients, sedation was used in the previous admission for ventilation and patient comfort (66.67%). Most of the readmitted patients (68.51%) have a previous ICU stay of more than five days. Patients were readmitted mainly because of respiratory (30.56%) and neurological (25%) complications. In this study, readmitted patients have high mortality (59.26%). Conclusion In a tertiary care ICU, the incidence rate of readmitted patients was 10.4%. Respiratory and neurological problems were the main cause of readmission. In readmitted patients, mortality was high up to 59.26%. Old age, male sex, prolonged ICU stay, comorbidities like hypertension, blunt trauma abdomen, use of sedation, and prolonged mechanical ventilation in previous ICU admission are major risk factors for ICU readmission.

Systematic Review of the Impact of Physician Work Schedules on Patient Safety with Meta-Analyses of Mortality Risk

Resident physician work hour limits continue to be controversial. Numerous trials have come to conflicting conclusions about the impact on patient safety of eliminating extended duration work shifts. We conducted meta-analyses to evaluate the impact of work hour policies and work schedules on patient safety. After identifying 8,362 potentially relevant studies and reviewing 688 full-text articles, 132 studies were retained and graded on quality of evidence. Of these, 68 studies provided enough information for consideration in meta-analyses. We found that patient safety improved following implementation of the Accreditation Council for Graduate Medical Education's 2003 and 2011 resident physicians work hour guidelines. Limiting all resident physicians to 80-hour work weeks and 28-hour shifts in 2003 was associated with an 11% reduction in mortality (p < 0.001). Limited shift durations and shorter work weeks were also associated with improved patient safety in clinical trials and observational studies not specifically tied to policy changes. Given the preponderance of evidence showing that patient and physician safety is negatively affected by long work hours, efforts to improve physician schedules should be prioritized. Policies that enable extended-duration shifts and long work weeks should be reexamined. Further research should expand beyond resident physicians to additional study populations, including attending physicians and other health care workers.

Incidence, risk and impact of ICU readmission on patient outcomes and resource utilisation in tertiary level ICUs in Nepal: A cohort study

Background: Readmissions to Intensive Care Units (ICUs) result in increased morbidity, mortality, and ICU resource utilisation (e.g. prolonged mechanical ventilation), and as such, is a widely utilised metric of quality of critical care. Most of the evidence on incidence, characteristics, associated risk factors and attributable outcomes of unplanned readmission to ICU are from studies performed in high-income countries This study explores the determinants of risk attributable to unplanned ICU readmission in four ICUs in Kathmandu, Nepal. Methods: The registry-embedded eCRF reported data on case mix, severity of illness, in-ICU interventions (including organ support), ICU outcome, and readmission characteristics. Data were captured in all adult patients admitted between September 2019 and February 2021. Population and ICU encounter characteristics were compared between those with and without readmission. Independent risk factors for readmission were assessed using univariate analysis. Results: In total 2955 patients were included in the study. Absolute unplanned ICU readmission rate was 5.69 % (n=168) for all four ICUs. Median time from ICU discharge to readmission was 3 days (IQR=8,1). Of those readmitted, 29.17% (n=49) were discharged at night following their index admission. ICU mortality was higher following readmission to ICU(p=0.016) and mortality was increased further in patients whose primary index discharge was at night(p= 0.019).  Primary diagnosis, age, and use of organ support in the first 24hrs of index admission were all independently attributable risk factors for readmission. Conclusions: Unplanned ICU readmission rates were adversely associated with significantly poorer outcomes, increased ICU resource utilisation. Clinical and organisational characteristics influenced risk of readmission and outcome.

Incidence, risk and impact of unplanned ICU readmission on patient outcomes and resource utilisation in tertiary level ICUs in Nepal: A cohort study

Background: Unplanned readmissions to Intensive Care Units (ICUs) result in increased morbidity, mortality, and ICU resource utilisation (e.g. prolonged mechanical ventilation), and as such, is a widely utilised metric of quality of critical care. Most of the evidence on incidence, characteristics, associated risk factors and attributable outcomes of unplanned readmission to ICU are from studies performed in high-income countries This study explores the determinants of risk attributable to unplanned ICU readmission in four ICUs in Kathmandu, Nepal. Methods: The registry-embedded eCRF reported data on case mix, severity of illness, in-ICU interventions (including organ support), ICU outcome, and readmission characteristics. Data were captured in all adult patients admitted between September 2019 and February 2021. Population and ICU encounter characteristics were compared between those with and without readmission. Independent risk factors for readmission were assessed using univariate analysis. Results: In total 2948 patients were included in the study. Absolute unplanned ICU readmission rate was 5.60 % (n=165) for all four ICUs. Median time from ICU discharge to readmission was 3 days (IQR=8,1). Of those readmitted, 29.7% (n=49) were discharged at night following their index admission. ICU mortality was higher following readmission to ICU(p=0.016) and mortality was increased further in patients whose primary index discharge was at night(p= 0.019).  Primary diagnosis, age, and use of organ support in the first 24hrs of index admission were all independently attributable risk factors for readmission. Conclusions: Unplanned ICU readmission rates were adversely associated with significantly poorer outcomes, increased ICU resource utilisation. Clinical and organisational characteristics influenced risk of readmission and outcome.

Development of a quality indicator set to measure and improve quality of ICU care in low- and middle-income countries

PURPOSE

To develop a set of actionable quality indicators for critical care suitable for use in low- or middle-income countries (LMICs).

METHODS

A list of 84 candidate indicators compiled from a previous literature review and stakeholder recommendations were categorised into three domains (foundation, process, and quality impact). An expert panel (EP) representing stakeholders from critical care and allied specialties in multiple low-, middle-, and high-income countries was convened. In rounds one and two of the Delphi exercise, the EP appraised (Likert scale 1-5) each indicator for validity, feasibility; in round three sensitivity to change, and reliability were additionally appraised. Potential barriers and facilitators to implementation of the quality indicators were also reported in this round. Median score and interquartile range (IQR) were used to determine consensus; indicators with consensus disagreement (median < 4, IQR ≤ 1) were removed, and indicators with consensus agreement (median ≥ 4, IQR ≤ 1) or no consensus were retained. In round four, indicators were prioritised based on their ability to impact cost of care to the provider and recipient, staff well-being, patient safety, and patient-centred outcomes.

RESULTS

Seventy-one experts from 30 countries (n = 45, 63%, representing critical care) selected 57 indicators to assess quality of care in intensive care unit (ICU) in LMICs: 16 foundation, 27 process, and 14 quality impact indicators after round three. Round 4 resulted in 14 prioritised indicators. Fifty-seven respondents reported barriers and facilitators, of which electronic registry-embedded data collection was the biggest perceived facilitator to implementation (n = 54/57, 95%) Concerns over burden of data collection (n = 53/57, 93%) and variations in definition (n = 45/57, 79%) were perceived as the greatest barrier to implementation.

CONCLUSION

This consensus exercise provides a common set of indicators to support benchmarking and quality improvement programs for critical care populations in LMICs.

Hospital Readmission and Post-Acute Care Use After Intensive Care Unit Admissions: New ICU Quality Metrics?

OBJECTIVE

Care coordination is a national priority. Post-acute care use and hospital readmission appear to be common after critical illness. It is unknown whether specialty critical care units have different readmission rates and what these trends have been over time.

METHODS

In this retrospective cohort study, a cohort of 53,539 medical/surgical patients who were treated in a critical care unit during their index admission were compared with 209,686 patients who were not treated in a critical care unit. The primary outcome was 30-day all cause hospital readmission. Secondary outcomes included post-acute care resource use and immediate readmission, defined as within 7 days of discharge.

RESULTS

Compared to patients discharged after an index hospitalization without critical illness, surviving patients following ICU admission were not more likely to be rehospitalized within 30 days (15.8 vs. 16.1%, p = 0.08). However, they were more likely to receive post-acute care services (45.3% vs. 70.9%, p < 0.001) as well as be rehospitalized within 7 days (5.2 vs. 6.0%, p < 0.001). Post-acute care use and 30-day readmission rates varied by ICU type, the latter ranging from 11.7% after admission in a cardiothoracic critical care unit to 23.1% after admission in a medical critical care unit. 30-day readmission after ICU admission did not decline between 2010 and 2015 (p = 0.38). Readmission rates declined over time for 2 of 4 targeted conditions (heart failure and chronic obstructive pulmonary disease), but only when the hospitalization did not include ICU admission.

CONCLUSIONS

Rehospitalization for survivors following ICU admission is common across all specialty critical care units. Post-acute care use is also common for this population of patients. Overall trends for readmission rates after critical illness did not change over time, and readmission reductions for targeted conditions were limited to hospitalizations that did not include an ICU admission.

A scoping review of registry captured indicators for evaluating quality of critical care in ICU

BACKGROUND

Excess morbidity and mortality following critical illness is increasingly attributed to potentially avoidable complications occurring as a result of complex ICU management (Berenholtz et al., J Crit Care 17:1-2, 2002; De Vos et al., J Crit Care 22:267-74, 2007; Zimmerman J Crit Care 1:12-5, 2002). Routine measurement of quality indicators (QIs) through an Electronic Health Record (EHR) or registries are increasingly used to benchmark care and evaluate improvement interventions. However, existing indicators of quality for intensive care are derived almost exclusively from relatively narrow subsets of ICU patients from high-income healthcare systems. The aim of this scoping review is to systematically review the literature on QIs for evaluating critical care, identify QIs, map their definitions, evidence base, and describe the variances in measurement, and both the reported advantages and challenges of implementation.

METHOD

We searched MEDLINE, EMBASE, CINAHL, and the Cochrane libraries from the earliest available date through to January 2019. To increase the sensitivity of the search, grey literature and reference lists were reviewed. Minimum inclusion criteria were a description of one or more QIs designed to evaluate care for patients in ICU captured through a registry platform or EHR adapted for quality of care surveillance.

RESULTS

The search identified 4780 citations. Review of abstracts led to retrieval of 276 full-text articles, of which 123 articles were accepted. Fifty-one unique QIs in ICU were classified using the three components of health care quality proposed by the High Quality Health Systems (HQSS) framework. Adverse events including hospital acquired infections (13.7%), hospital processes (54.9%), and outcomes (31.4%) were the most common QIs identified. Patient reported outcome QIs accounted for less than 6%. Barriers to the implementation of QIs were described in 35.7% of articles and divided into operational barriers (51%) and acceptability barriers (49%).

CONCLUSIONS

Despite the complexity and risk associated with ICU care, there are only a small number of operational indicators used. Future selection of QIs would benefit from a stakeholder-driven approach, whereby the values of patients and communities and the priorities for actionable improvement as perceived by healthcare providers are prioritized and include greater focus on measuring discriminable processes of care.

Resident Working Hour Restrictions Increased the Workload of the Medical Emergency Team: A Retrospective Observational Study

BACKGROUND

Restrictions to residents' working hours have been shown to increase the workload of other medical resources; few studies have measured the effects on medical emergency teams (METs).

OBJECTIVES

This study evaluated how limiting residents' working hours affected the workload of MET in a pulmonology unit.

METHODS

This retrospective observational study analyzed MET activity during periods before and after we limited the working hours of residents in our pulmonary unit to 88 h/wk: Period 1, March 2014 to February 2015; and Period 2, March 2015 to February 2016. Medical emergency team activities, dose (activations/1000 admissions), intensive care unit transfers, and mortality were compared between the two periods for weekdays and for weekends and holidays.

RESULTS

There were no significant differences between the two periods in MET dose (85.0 in Period 1 versus 91.3 in Period 2, P = 0.675), intensive care unit transfers (P = 0.828), 30-day mortality (P = 0.701), and 60-day mortality (P = 0.531). However, some activities increased significantly or near significantly in Period 2, including portable echocardiography (P < 0.001), arterial line insertion (P = 0.034), mechanical ventilation (P = 0.063), and fluid therapy (P = 0.220). These increases were greater for weekends and holidays than for weekdays.

CONCLUSIONS

Since December 2017, a specific law for improving the training environment and status of residents has been implemented and applied at all hospitals in Korea. This legal restriction to working hours raises concerns regarding other medical personnel and system improvements to ensure patient safety and care continuity.

Preventing Early Bouncebacks to the Neurointensive Care Unit: A Retrospective Analysis and Quality Improvement Pilot

BACKGROUND

Early unplanned readmissions of "bouncebacks" to intensive care units are a healthcare quality metric and result in higher mortality and greater cost. Few studies have examined bouncebacks to the neurointensive care unit (neuro-ICU), and we sought to design and implement a quality improvement pilot to reduce that rate.

METHODS

First, we performed a retrospective chart review of 504 transfers to identify potential bounceback risk factors. Risk factors were assessed on the day of transfer by the transferring physician identifying patients as "high risk" or "low risk" for bounceback. "High-risk" patients underwent an enhanced transfer process emphasizing interdisciplinary communication and rapid assessment upon transfer during a 9-month pilot.

RESULTS

Within the retrospective cohort, 34 of 504 (4.7%) transfers required higher levels of care within 48 h. Respiratory failure and sepsis/hypotension were the most common reasons for bounceback among this group. During the intervention, 8 of 225 (3.6%) transfers bounced back, all of who were labeled "high risk." Being "high risk" was associated with a risk of bounceback (OR not calculable, p = 0.02). Aspiration risk (OR 6.9; 95% CI 1.6-30, p = 0.010) and cardiac arrhythmia (OR 7.1; 95% CI 1.6-32, p = 0.01) were independent predictors of bounceback in multivariate analysis. Bounceback rates trended downward to 2.8% in the final phase (p for trend 0.09). Eighty-five percent of providers responded that the pilot should become standard of care.

CONCLUSION

Patients at high risk for bounceback after transfer from the neuro-ICU can be identified using a simple tool. Early augmented multidisciplinary communication and care for high-risk patients may improve their management in the hospital.

Risk prediction models for intensive care unit readmission: A systematic review of methodology and applicability

OBJECTIVE

We conducted a systematic review of primary models to predict intensive care unit (ICU) readmission.

REVIEW METHODS

We searched MEDLINE, PubMed, Scopus, and Embase for studies on the development of ICU readmission prediction models that are published until January 2017. Data were extracted on the source of data, participants, outcomes, candidate predictors, sample size, missing data, methods for model development, and measures of model performance and model evaluation. The quality and applicability of the included studies were assessed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies.

RESULTS

We identified five studies describing the development of the primary prediction models of ICU readmission. Studies ranged in size from 343 to 704,963 patients with the mean age of 58.0-68.9 years. The proportion of readmission ranged from 2.5% to 9.6%. The discriminative ability of prediction models measured by area under the receiver operating characteristic curve was 0.66-0.81. None of the studies performed external validations. The quality scores ranged from 42 to 54 out of 62, and the applicability scores from 24 to 32 out of 38.

CONCLUSION

We identified five prediction models for ICU readmission. However, owing to the numerous methodological and reporting deficiencies in the included studies, physicians using these models should interpret the predictions with precautions until an external validation study shows the acceptable level of calibration and accuracy of these models.

Should Emergency Department Attendances be Used With or Instead of Readmission Rates as a Performance Metric?: Comparison of Statistical Properties Using National Data

BACKGROUND

Hospital readmissions are common and are viewed as unfavorable. They are commonly used as a measure of quality of care and, in the United States and England, are associated with financial penalties. Readmissions are not the only possible return-to-acute-care metric; patients may also attend emergency departments (EDs).

OBJECTIVE

To assess hospital-level return-to-acute-care metrics using statistical criteria.

RESEARCH DESIGN

Patient readmissions and/or ED attendances were aggregated to produce risk-standardized hospital rates. Return-to-acute-care rates at 7, 30, 90, and 365 days were assessed using key statistical properties: (i) variability between hospitals; (ii) the relative contribution of patient and nonpatient factors to variation; and (iii) the statistical power to detect performance differences.

SUBJECTS

We had pseudonymized administrative data on all inpatient hospital admissions and ED attendances in National Health Service hospitals in England between April 2009 and March 2011. Patients with an inpatient stay for chronic obstructive pulmonary disorder or heart failure were eligible for inclusion.

MEASURES

ED attendances and readmissions for patients discharged from an inpatient stay for chronic obstructive pulmonary disorder or heart failure.

RESULTS

Interhospital variation was greatest for ED attendance; in addition, readmission was more strongly determined by patient characteristics than was ED attendance or both combined. Because of smaller numbers, the statistical power to detect differences in rates at 7 days for any indicator was limited.

CONCLUSIONS

Despite the current emphasis on readmissions, we found that ED attendance within 30 days has more desirable statistical properties and therefore the potential to be a useful metric when comparing hospitals.

Development and implementation of a risk identification tool to facilitate critical care transitions for high-risk surgical patients

Quality problem

Patients recently discharged from the intensive care unit (ICU) are at high risk for clinical deterioration.

Initial assessment

Unreliable and incomplete handoffs of complex patients contributed to preventable ICU readmissions. Respiratory decompensation was responsible for four times as many readmissions as other causes.

Choice of solution

Form a multidisciplinary team to address care coordination surrounding the transfer of patients from the ICU to the surgical ward.

Implementation

A quality improvement intervention incorporating verbal handoffs, time-sensitive patient evaluations and visual cues was piloted over a 1-year period in consecutive high-risk surgical patients discharged from the ICU. Process metrics and clinical outcomes were compared to historical controls.

Evaluation

The intervention brought the primary team and respiratory therapists to the bedside for a baseline examination within 60 min of ward arrival. Stakeholders viewed the intervention as such a valuable adjunct to patient care that the intervention has become a standard of care. While not significant, in a comparatively older and sicker intervention population, the rate of readmissions due to respiratory decompensation was 12.5%, while 35.0% in the control group (P = 0.28).

Lessons learned

The implementation of this ICU transition protocol is feasible and internationally applicable, and results in improved care coordination and communication for a high-risk group of patients.

Preventable readmission to intensive care unit in critically ill cancer patients

BACKGROUND

Readmission to intensive care unit (ICU) after discharge to ward has been reported to be associated with increased hospital mortality and longer length of stay (LOS). The objective of this study was to investigate whether ICU readmission are preventable in critically ill cancer patients.

METHODS

Data of patients who readmitted to intensive care unit (ICU) at National Cancer Center/Cancer Hospital of Chinese Academy of Medical Sciences (CAMS) and Peking Union Medical College (PUMC) between January 2013 and November 2016 were retrospectively collected and reviewed.

RESULTS

A total of 39 patients were included in the final analysis, and the overall readmission rate between 2013 and 2016 was 1.32% (39/2,961). Of 39 patients, 32 (82.1%) patients were judged as unpreventable and 7 (17.9%) patients were preventable. There were no significant differences in duration of mechanical ventilation, ICU LOS, hospital LOS, ICU mortality and in-hospital mortality between patients who were unpreventable and preventable. For 24 early readmission patients, 7 (29.2%) patients were preventable and 17 (70.8%) patients were unpreventable. Patients who were late readmission were all unpreventable. There was a trend that patients who were preventable had longer 1-year survival compared with patients who were unpreventable (100% vs. 66.8%, log rank=1.668, P=0.196).

CONCLUSION

Most readmission patients were unpreventable, and all preventable readmissions occurred in early period after discharge to ward. There were no significant differences in short term outcomes and 1-year survival in critically ill cancer patients whose readmissions were preventable or not.

Barriers and facilitators to improve safety and efficiency of the ICU discharge process: a mixed methods study

Background

Evidence indicates that suboptimal clinical handover from the intensive care unit (ICU) to general wards leads to unnecessary ICU readmissions and increased mortality. We aimed to gain insight into barriers and facilitators to implement and use ICU discharge practices.

Methods

A mixed methods approach was conducted, using 1) 23 individual and four focus group interviews, with post-ICU patients, ICU managers, and nurses and physicians working in the ICU or general ward of ten Dutch hospitals, and 2) a questionnaire survey, which contained 27 statements derived from the interviews, and was completed by 166 ICU physicians (21.8%) from 64 Dutch hospitals (71.1% of the total of 90 Dutch hospitals).

Results

The interviews resulted in 66 barriers and facilitators related to: the intervention (e.g., feasibility); the professional (e.g., attitude towards checklists); social factors (e.g., presence or absence of a culture of feedback); and the organisation (e.g., financial resources). A facilitator considered important by ICU physicians was a checklist to structure discharge communication (92.2%). Barriers deemed important were lack of a culture of feedback (55.4%), an absence of discharge criteria (23.5%), and an overestimation of the capabilities of general wards to care for complex patients by ICU physicians (74.7%).

Conclusions

Based on the barriers and facilitators found in this study, improving handover communication, formulating specific discharge criteria, stimulating a culture of feedback, and preventing overestimation of the general ward are important to effectively improve the ICU discharge process.

Electronic supplementary material

The online version of this article (doi:10.1186/s12913-017-2139-x) contains supplementary material, which is available to authorized users.

Who Dies after ICU Discharge? Retrospective Analysis of Prognostic Factors for In-Hospital Mortality of ICU Survivors

We investigated the causes of inpatient death after intensive care unit (ICU) discharge and determined predictors of in-hospital mortality in Korea. Using medical ICU registry data of Seoul National University Hospital, we performed a retrospective cohort study involving patients who were discharged alive from their first ICU admission with at least 24 hours of ICU length of stay (LOS). From January 2011 to August 2013, 723 patients were admitted to ICU and 383 patients were included. The estimated in-hospital mortality rate was 11.7% (45/383). The most common cause of death was respiratory failure (n = 25, 56%) followed by sepsis and cancer progression; the causes of hospital death and ICU admission were the same in 64% of all deaths; sudden unexpected deaths comprised about one-fifth of all deaths. In order to predict in-hospital mortality among ICU survivors, multivariate analysis identified presence of solid tumor (odds ratio [OR], 4.06; 95% confidence interval [CI], 2.01-8.2; P < 0.001), hematologic disease (OR, 4.75; 95% CI, 1.51-14.96; P = 0.013), Sequential Organ Failure Assessment (SOFA) score upon ICU admission (OR, 1.08; 95% CI, 0.99-1.17; P = 0.075), and hemoglobin (Hb) level (OR, 0.67; 95% CI, 0.52-0.86; P = 0.001) and platelet count (Plt) (OR, 0.99; 95% CI, 0.99-1.00; P = 0.033) upon ICU discharge as significant factors. In conclusion, a significant proportion of in-hospital mortality is predictable and those who die in hospital after ICU discharge tend to be severely-ill, with comorbidities of hematologic disease and solid tumor, and anemic and thrombocytopenic upon ICU discharge.

The Impact of the 2011 Accreditation Council for Graduate Medical Education Duty Hour Reform on Quality and Safety in Trauma Care

BACKGROUND

In 2011, the ACGME limited duty hours for residents. Although studies evaluating the 2011 policy have not shown improvements in general measures of morbidity or mortality, these outcomes might not reflect changes in specialty-specific practice patterns and secondary quality measures.

STUDY DESIGN

All trauma admissions from July 2009 through June 2013 at an academic Level I trauma center were evaluated for 5 primary outcomes (eg, mortality and length of stay), and 10 secondary quality measures and practice patterns (eg, operating room [OR] visits). All variables were compared before and after the reform (July 1, 2011). Piecewise regression was used to study temporal trends in quality.

RESULTS

There were 11,740 admissions studied. The reform was not strongly associated with changes in any primary outcomes except length of stay (7.98 to 7.36 days; p = 0.01). However, many secondary quality metrics changed. The total number of OR and bedside procedures per admission (6.72 to 7.34; p < 0.001) and OR visits per admission (0.76 to 0.91; p < 0.001) were higher in the post-reform group, representing an additional 9,559 procedures and 1,584 OR visits. Use of minor bedside procedures, such as laboratory and imaging studies, increased most significantly.

CONCLUSIONS

Although most major outcomes were unaffected, quality of care might have changed after the reform. Indeed, a consistent change in resource use patterns was manifested by substantial post-reform increases in measures such as bedside procedures and OR visits. No secondary quality measures exhibited improvements strongly associated with the reform. Several factors, including attending oversight, might have insulated major outcomes from change. Our findings show that some less-commonly studied quality metrics related to costs of care changed after the 2011 reform at our institution.