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Objective: This pilot study aimed to explore knowledge, attitudes and confidence levels of Obstructive Sleep Apnoea (OSA) before and after case-based education among dental hygiene students. Moreover, to give an international perspective, this study included students from the US and Scandinavia., Methods: A longitudinal multicentre study assessed dental hygiene students' OSA knowledge, attitudes and confidence through a case-based education model. Students from six dental hygiene programmes in the US, Norway and Sweden participated from 2021 to 2022. The intervention group (N = 89) received preview material, live synchronous OSA lecture, case-based screening of fictitious patients and clinical practice using the STOP-Bang screening tool. Controls (N = 70) followed the standard curriculum. The Obstructive Sleep Apnoea Knowledge and Attitudes (OSAKA) questionnaire, comprising 18 knowledge and five attitude/confidence questions, was used. Reliability was assessed and nonparametric tests determined OSAKA knowledge score differences among the baseline, 6 months and 12 months intervals., Results: The intervention increased OSA knowledge with the largest increase in proportions of correct responses occurring between the baseline and 6 months and all single items showing a higher proportion of correct answers at 12 months compared to the baseline. Students' attitudes regarding the importance of OSAKA and confidence in managing patients increased from the baseline to both 6 months and 12 months., Conclusion: The case based educational intervention increased OSA knowledge and improved attitudes and confidence among dental hygiene students. The results also determined that the OSAKA questionnaire can be used in a setting with dental hygiene students to provide an overview of their knowledge, attitudes and confidence toward OSA. Current sleep medicine education in dental hygiene programmes is limited, suggesting the need for enhanced and sustained educational interventions to address this knowledge gap., (© 2024 The Author(s). International Journal of Dental Hygiene published by John Wiley & Sons Ltd.)

Background: The last national estimates of US ICU physician staffing are 25 years old and lack information about interprofessional teams., Research Question: How are US adult ICUs currently staffed?, Study Design and Methods: We conducted a cross-sectional survey (May 4, 2022-February 2, 2023) of adult ICU clinicians (targeting nurse/physician leadership) contacted using 2020 American Hospital Association (AHA) database information and, secondarily, through professional organizations. The survey included questions about interprofessional ICU staffing availability and roles at steady state (pre-COVID-19). We linked survey data to hospital data in the AHA database to create weighted national estimates by extrapolating ICU staffing data to nonrespondent hospitals based on hospital characteristics., Results: The cohort consisted of 596 adult ICUs (response rates: AHA contacts: 2.1%; professional organizations: unknown) with geographic diversity and size variability (median, 20 beds; interquartile range, 12-25); most cared for mixed populations (414 [69.5%]), yet medical (55 [9.2%]), surgical (70 [11.7%]), and specialty (57 [9.6%]) ICUs were well represented. A total of 554 (93.0%) had intensivists available, with intensivists covering all patients in 75.6% of these and onsite 24 h/d in one-half (53.3% weekdays; 51.8% weekends). Of all ICUs, 69.8% had physicians-in-training and 77.7% had nurse practitioners/physician assistants. For patients on mechanical ventilation, nurse to patient ratios were 1:2 in 89.6% of ICUs. Clinical pharmacists were available in 92.6%, and respiratory therapists were available in 98.8%. We estimated 85.1% (95% CI, 85.7%-84.5%) of hospitals nationally had ICUs with intensivists, 51.6% (95% CI, 50.6%-52.5%) had physicians-in-training, 72.1% (95% CI, 71.3%-72.9%) had nurse practitioners/physician assistants, 98.5% (95% CI, 98.4%-98.7%) had respiratory therapists, and 86.9% (95% CI, 86.4%-87.4%) had clinical pharmacists. For patients on mechanical ventilation, 86.4% (95% CI, 85.8%-87.0%) used 1:2 nurses/patients., Interpretation: We found that intensivist presence in adult US ICUs has greatly increased over 25 years. Intensivists, respiratory therapists, and clinical pharmacists are commonly available, and each nurse usually provides care for two patients on mechanical ventilation. However, team composition and workload vary., Competing Interests: Financial/Nonfinancial Disclosures The authors have reported to CHEST the following: H. B. G. received funds from Gilead Sciences, Inc to serve as a scientific advisor regarding COVID-19 therapeutics and is currently Editor in Chief of CHEST Critical Care. None declared (A. G., D. K. C., A. L. D., R. F., A. A. K., V. X. L., D. L., D. C. S., H. W.)., (Copyright © 2024 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Objective: Predictive models developed for use in ICUs have been based on retrospectively collected data, which does not take into account the challenges associated with live, clinical data. This study sought to determine if a previously constructed predictive model of ICU mortality (ViSIG) is robust when using data collected prospectively in near real-time., Design: Prospectively collected data were aggregated and transformed to evaluate a previously developed rolling predictor of ICU mortality., Setting: Five adult ICUs at Robert Wood Johnson-Barnabas University Hospital and one adult ICU at Stamford Hospital., Patients: One thousand eight hundred and ten admissions from August to December 2020., Measurements and Main Results: The ViSIG Score, comprised of severity weights for heart rate, respiratory rate, oxygen saturation, mean arterial pressure, mechanical ventilation, and values for OBS Medical's Visensia Index. This information was collected prospectively, whereas data on discharge disposition was collected retrospectively to measure the ViSIG Score's accuracy. The distribution of patients' maximum ViSIG Score was compared with ICU mortality rate, and cut points determined where changes in mortality probability were greatest. The ViSIG Score was validated on new admissions. The ViSIG Score was able to stratify patients into three groups: 0-37 (low risk), 38-58 (moderate risk), and 59-100 (high risk), with mortality of 1.7%, 12.0%, and 39.8%, respectively ( p < 0.001). The sensitivity and specificity of the model to predict mortality for the high-risk group were 51% and 91%. Performance on the validation dataset remained high. There were similar increases across risk groups for length of stay, estimated costs, and readmission., Conclusions: Using prospectively collected data, the ViSIG Score produced risk groups for mortality with good sensitivity and excellent specificity. A future study will evaluate making the ViSIG Score visible to clinicians to determine whether this metric can influence clinician behavior to reduce adverse outcomes., Competing Interests: Dr. Lissauer’s institution received funding from Prescient Healthcare Consulting; he received funding from Leon Piechta Esq, Lacava, Jacobs and Goodis, Saltz Mongeluzzi and Bendesky, Rugers University, and Hartford Healthcare Medical Group. The remaining authors have not disclosed any potential conflicts of interest., (Copyright © 2023 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

P/Q-type Ca 2+ flux into nerve terminals via Ca V 2.1 channels is essential for neurotransmitter release at neuromuscular junctions and nearly all central synapses. Mutations in CACNA1A , the gene encoding Ca V 2.1, cause a spectrum of pediatric neurological disorders. We have identified a patient harboring an autosomal-dominant de novo frameshift-causing nucleotide duplication in CACNA1A (c.5018dupG). The duplicated guanine precipitated 43 residues of altered amino acid sequence beginning with a glutamine to serine substitution in Ca V 2.1 at position 1674 ending with a premature stop codon (Ca V 2.1 p.Gln1674Serfs*43). The patient presented with episodic downbeat vertical nystagmus, hypotonia, ataxia, developmental delay and febrile seizures. In patch-clamp experiments, no Ba 2+ current was observed in tsA-201 cells expressing Ca V 2.1 p.Gln1674Serfs*43 with β 4 and α 2 δ-1 auxiliary subunits. The ablation of divalent flux in response to depolarization was likely attributable to the inability of Ca V 2.1 p.Gln1674Serfs*43 to form a complete channel pore. Our results suggest that the pathology resulting from this frameshift-inducing nucleotide duplication is a consequence of an effective haploinsufficiency., Competing Interests: The authors declared no conflicts of interest with respect to the authorship and/or publication of this article., (© 2023 The Authors.)

P/Q-type Ca 2+ currents mediated by Ca V 2.1 channels are essential for active neurotransmitter release at neuromuscular junctions and many central synapses. Mutations in CACNA1A, the gene encoding the principal Ca V 2.1 α 1A subunit, cause a broad spectrum of neurological disorders. Typically, gain-of-function (GOF) mutations are associated with migraine and epilepsy while loss-of-function (LOF) mutations are causative for episodic and congenital ataxias. However, a cluster of severe Ca V 2.1 channelopathies have overlapping presentations which suggests that channel dysfunction in these disorders cannot always be defined bimodally as GOF or LOF. In particular, the R1667P mutation causes focal seizures, generalized hypotonia, dysarthria, congenital ataxia and, in one case, cerebral edema leading ultimately to death. Here, we demonstrate that the R1667P mutation causes both channel GOF (hyperpolarizing voltage-dependence of activation, slowed deactivation) and LOF (slowed activation kinetics) when expressed heterologously in tsA-201 cells. We also observed a substantial reduction in Ca 2+ current density in this heterologous system. These changes in channel gating and availability/expression manifested in diminished Ca 2+ flux during action potential-like stimuli. However, the integrated Ca 2+ fluxes were no different when normalized to tail current amplitude measured upon repolarization from the reversal potential. In summary, our findings indicate a complex functional effect of R1667P and support the idea that pathological missense mutations in Ca V 2.1 may not represent exclusively GOF or LOF., (© 2022. The Author(s).)

Axons in the corticospinal tract (CST) display a limited capacity for compensatory sprouting after partial spinal injuries, potentially limiting functional recovery. Forced expression of a developmentally expressed transcription factor, Krüppel-like factor 6 (KLF6), enhances axon sprouting by adult CST neurons. Here, using a pyramidotomy model of injury in adult mice, we confirm KLF6's pro-sprouting properties in spared corticospinal tract neurons and show that this effect depends on an injury stimulus. In addition, we probed the time course of KLF6-triggered sprouting of CST axons and demonstrate a significant enhancement of growth within four weeks of treatment. Finally, we tested whether KLF6-induced sprouting was accompanied by improvements in forelimb function, either singly or when combined with intensive rehabilitation. We found that regardless of rehabilitative training, and despite robust cross-midline sprouting by corticospinal tract axons, treatment with KLF6 produced no significant improvement in forelimb function on either a modified ladder-crossing task or a pellet-retrieval task. These data clarify important details of KLF6's pro-growth properties and indicate that additional interventions or further optimization will be needed to translate this improvement in axon growth into functional gains., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Early Warning Scores (EWS) are a composite evaluation of a patient's basic physiology, changes of which are the first indicators of clinical decline and are used to prompt further patient assessment and when indicated intervention. These are sometimes referred to as "track and triggers systems" with tracking meant to denote periodic observation of physiology and trigger being a predetermined response criteria. This review article examines the most widely used EWS, with special attention paid to those used in military and trauma populations.The earliest EWS is the Modified Early Earning Score (MEWS). In MEWS, points are allocated to vital signs based on their degree of abnormality, and summed to yield an aggregate score. A score above a threshold would elicit a clinical response such as a rapid response team. Modified Early Earning Score was subsequently followed up with the United Kingdom's National Early Warning Score, the electronic cardiac arrest triage score, and the 10 Signs of Vitality score, among others.Severity of illness indicators have been in military and civilian trauma populations, such as the Revised Trauma Score, Injury Severity Score, and Trauma and Injury Severity. The sequential organ failure assessment score and its attenuated version quick sequential organ failure assessment were developed to aggressively identify patients near septic shock.Effective EWS have certain characteristics. First, they should accurately capture vital signs information. Second, almost all data should be derived electronically rather than manually. Third, the measurements should take into consideration multiple organ systems. Finally, information that goes into an EWS must be captured in a timely manner. Future trends include the use of machine learning to detect subtle changes in physiology and the inclusion of data from biomarkers. As EWS improve, they will be more broadly used in both military and civilian environments. LEVEL OF EVIDENCE: Review article, level I.

Objectives: The high cost of critical care has engendered research into identifying influential factors. However, existing studies have not considered patient vital status at ICU discharge. This study sought to determine the effect of mortality upon the total cost of an ICU stay., Design: Retrospective cohort study., Setting: Twenty-six ICUs at 13 hospitals in the United States., Patients: 58,344 admissions from January 1, 2012, to June 30, 2016, obtained from a commercial ICU database., Interventions: None., Measurements and Main Results: The median observed cost of a unit stay was $9,619 (mean = $16,353). A multivariable regression model was developed on the log of total costs for a unit stay, using severity of illness, unit admitting diagnosis, mortality in the unit, daily unit occupancy (occupying a bed at midnight), and length of mechanical ventilation. This model had an r of 0.67 and a median difference between observed and expected costs of $437. The first few days of care and the first day receiving mechanical ventilation had the largest effect on total costs. Patients dying before unit discharge had 12.4% greater costs than survivors (p < 0.01; 99% CI = 9.3-15.5%) after multivariable adjustment. This effect was most pronounced for patients with an extended ICU stay who were receiving mechanical ventilation., Conclusions: While the largest drivers of ICU costs at the patient level are day 1 room occupancy and day 1 mechanical ventilation, mortality before unit discharge is associated with substantially higher costs. The increase was most evident for patients with an extended ICU stay who were receiving mechanical ventilation. Studies evaluating costs among ICUs need to take mortality into account.

Objective: Reintubation after liberation from mechanical ventilation is viewed as an adverse event in ICUs. We sought to describe the frequency of reintubations across U.S. ICUs and to propose a standard, appropriate time cutoff for reporting of reintubation events., Design and Setting: We conducted a cohort study using data from the Project IMPACT database of 185 diverse ICUs in the United States., Patients: We included patients who received mechanical ventilation and excluded patients who received a tracheostomy, had a do-not-resuscitate order placed, or died prior to first extubation., Measurements and Main Results: We assessed the percentage of patients extubated who were reintubated; the cumulative probability of reintubation, with death and do-not-resuscitate orders after extubation modeled as competing risks, and time to reintubation. Among 98,367 patients who received mechanical ventilation without death or tracheostomy prior to extubation, 9,907 (10.1%) were reintubated, with a cumulative probability of 10.0%. Median time to reintubation was 15 hours (interquartile range, 2-45 hr). Of patients who required reintubation in the ICU, 90% did so within the first 96 hours after initial extubation; this was consistent across various patient subtypes (89.3% for electives surgical patients up to 94.8% for trauma patients) and ICU subtypes (88.6% for cardiothoracic ICUs to 93.5% for medical ICUs)., Conclusions: The reintubation rate for ICU patients liberated from mechanical ventilation in U.S. ICUs is approximately 10%. We propose a time cutoff of 96 hours for reintubation definitions and benchmarking efforts, as it captures 90% of ICU reintubation events. Reintubation rates can be reported as simple percentages, without regard for deaths or changes in goals of care that might occur.

Objectives: To examine ICU performance based on the Simplified Acute Physiology Score 3 using 30-day, 90-day, or 180-day mortality as outcome measures and compare results with 30-day mortality as reference., Design: Retrospective cohort study of ICU admissions from 2010 to 2014., Setting: Sixty-three Swedish ICUs that submitted data to the Swedish Intensive Care Registry., Patients: The development cohort was first admissions to ICU during 2011-2012 (n = 53,546), and the validation cohort was first admissions to ICU during 2013-2014 (n = 57,729)., Interventions: None., Measurements and Main Results: Logistic regression was used to develop predictive models based on a first level recalibration of the original Simplified Acute Physiology Score 3 model but with 30-day, 90-day, or 180-day mortality as measures of outcome. Discrimination and calibration were excellent for the development dataset. Validation in the more recent 2013-2014 database showed good discrimination (C-statistic: 0.85, 0.84, and 0.83 for the 30-, 90-, and 180-d models, respectively), and good calibration (standardized mortality ratio: 0.99, 0.99, and 1.00; Hosmer-Lemeshow goodness of fit H-statistic: 66.4, 63.7, and 81.4 for the 30-, 90-, and 180-d models, respectively). There were modest changes in an ICU's standardized mortality ratio grouping (< 1.00, not significant, > 1.00) when follow-up was extended from 30 to 90 days and 180 days, respectively; about 11-13% of all ICUs., Conclusions: The recalibrated Simplified Acute Physiology Score 3 hospital outcome prediction model performed well on long-term outcomes. Evaluation of ICU performance using standardized mortality ratio was only modestly sensitive to the follow-up time. Our results suggest that 30-day mortality may be a good benchmark of ICU performance. However, the duration of follow-up must balance between what is most relevant for patients, most affected by ICU care, least affected by administrative policies and practically feasible for caregivers.

Objectives: To develop a model that predicts the duration of mechanical ventilation and then to use this model to compare observed versus expected duration of mechanical ventilation across ICUs., Design: Retrospective cohort analysis., Setting: Eighty-six eligible ICUs at 48 U.S. hospitals., Patients: ICU patients receiving mechanical ventilation on day 1 (n = 56,336) admitted from January 2013 to September 2014., Interventions: None., Measurements and Main Results: We developed and validated a multivariable logistic regression model for predicting duration of mechanical ventilation using ICU day 1 patient characteristics. Mean observed minus expected duration of mechanical ventilation was then obtained across patients and for each ICU. The accuracy of the model was assessed using R. We defined better performing units as ICUs that had an observed minus expected duration of mechanical ventilation less than -0.5 days and a p value of less than 0.01; and poorer performing units as ICUs with an observed minus expected duration of mechanical ventilation greater than +0.5 days and a p value of less than 0.01. The factors accounting for the majority of the model's explanatory power were diagnosis (71%) and physiologic abnormalities (24%). For individual patients, the difference between observed and mean predicted duration of mechanical ventilation was 3.3 hours (95% CI, 2.8-3.9) with R equal to 21.6%. The mean observed minus expected duration of mechanical ventilation across ICUs was 3.8 hours (95% CI, 2.1-5.5), with R equal to 69.9%. Among the 86 ICUs, 66 (76.7%) had an observed mean mechanical ventilation duration that was within 0.5 days of predicted. Five ICUs had significantly (p < 0.01) poorer performance (observed minus expected duration of mechanical ventilation, > 0.5 d) and 14 ICUs significantly (p < 0.01) better performance (observed minus expected duration of mechanical ventilation, < -0.5 d)., Conclusions: Comparison of observed and case-mix-adjusted predicted duration of mechanical ventilation can accurately assess and compare duration of mechanical ventilation across ICUs, but cannot accurately predict an individual patient's mechanical ventilation duration. There are substantial differences in duration of mechanical ventilation across ICU and their association with unit practices and processes of care warrants examination.

The data contained within the electronic health record (EHR) is "big" from the standpoint of volume, velocity, and variety. These circumstances and the pervasive trend towards EHR adoption have sparked interest in applying big data predictive analytic techniques to EHR data. Acute kidney injury (AKI) is a condition well suited to prediction and risk forecasting; not only does the consensus definition for AKI allow temporal anchoring of events, but no treatments exist once AKI develops, underscoring the importance of early identification and prevention. The Acute Dialysis Quality Initiative (ADQI) convened a group of key opinion leaders and stakeholders to consider how best to approach AKI research and care in the "Big Data" era. This manuscript addresses the core elements of AKI risk prediction and outlines potential pathways and processes. We describe AKI prediction targets, feature selection, model development, and data display.

Neurons in the embryonic and peripheral nervous system respond to injury by activating transcriptional programs supportive of axon growth, ultimately resulting in functional recovery. In contrast, neurons in the adult central nervous system (CNS) possess a limited capacity to regenerate axons after injury, fundamentally constraining repair. Activating pro-regenerative gene expression in CNS neurons is a promising therapeutic approach, but progress is hampered by incomplete knowledge of the relevant transcription factors. An emerging hypothesis is that factors implicated in cellular growth and motility outside the nervous system may also control axon growth in neurons. We therefore tested sixty-nine transcription factors, previously identified as possessing tumor suppressive or oncogenic properties in non-neuronal cells, in assays of neurite outgrowth. This screen identified YAP1 and E2F1 as enhancers of neurite outgrowth, and PITX1, RBM14, ZBTB16, and HHEX as inhibitors. Follow-up experiments are focused on the tumor suppressor HHEX, one of the strongest growth inhibitors. HHEX is widely expressed in adult CNS neurons, including corticospinal tract neurons after spinal injury, but is present only in trace amounts in immature cortical neurons and adult peripheral neurons. HHEX overexpression in early postnatal cortical neurons reduced both initial axonogenesis and the rate of axon elongation, and domain deletion analysis strongly implicated transcriptional repression as the underlying mechanism. These findings suggest a role for HHEX in restricting axon growth in the developing CNS, and substantiate the hypothesis that previously identified oncogenes and tumor suppressors can play conserved roles in axon extension., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Importance: Little is known about the deadoption of ineffective or harmful clinical practices. A large clinical trial (the Normoglycemia in Intensive Care Evaluation and Survival Using Glucose Algorithm Regulation [NICE-SUGAR] trial) demonstrated that strict blood glucose control (tight glycemic control) in patients admitted to adult intensive care units (ICUs) should be deadopted; however, it is unknown whether deadoption occurred and how it compared with the initial adoption., Objective: To evaluate glycemic control in critically ill patients before and after the publication of clinical trials that initially suggested that tight glycemic control reduced mortality (Leuven I) but subsequently demonstrated that it increased mortality (NICE-SUGAR)., Design, Setting, and Participants: Interrupted time-series analysis of 353,464 patients admitted to 113 adult ICUs from January 1, 2001, through December 31, 2012, in the United States using data from the Acute Physiology and Chronic Health Evaluation database., Main Outcomes and Measures: The physiologically most extreme blood glucose level on day 1 of ICU admission defined glycemic control as tight control (glucose level, 80-110 mg/dL; to convert to millimoles per liter, multiply by 0.0555), hypoglycemia (glucose level, <70 mg/dL), and hyperglycemia (glucose level, ≥180 mg/dL). Temporal changes in each marker were examined using mixed-effects segmented linear regression., Results: Before the publication of Leuven I, 17.2% (95% CI, 16.2%-18.2%) of ICU admissions had tight glycemic control, 3.0% (95% CI, 2.6%-3.5%) had hypoglycemia, and 40.2% (95% CI, 38.8%-41.5%) had hyperglycemia. After the publication of Leuven I, there were significant increases in the relative proportion of admissions with tight glycemic control (1.7% per quarter; 95% CI, 1.2%-2.3%; P<.001) and hypoglycemia (2.5% per quarter; 95% CI, 1.9%-3.2%; P<.001) and decreases in those with hyperglycemia (0.6% per quarter; 95% CI, 0.4%-0.9%; P<.001). Following the publication of NICE-SUGAR, there was no change in the proportion of patients with tight glycemic control or hyperglycemia. There was an immediate decrease in the relative proportion of patients with hypoglycemia (22.4%; 95% CI, 13.2%-30.1%; P<.001) but no subsequent change over time., Conclusions and Relevance: Among patients admitted to adult ICUs in the United States, there was a slow steady adoption of tight glycemic control following publication of a clinical trial that suggested benefit, with little to no deadoption following a subsequent trial that demonstrated harm. There is an urgent need to understand and promote the deadoption of ineffective clinical practices.

Objective: Impaired consciousness has been incorporated in prediction models that are used in the ICU. The Glasgow Coma Scale has value but is incomplete and cannot be assessed in intubated patients accurately. The Full Outline of UnResponsiveness score may be a better predictor of mortality in critically ill patients., Setting: Thirteen ICUs at five U.S. hospitals., Subjects: One thousand six hundred ninety-five consecutive unselected ICU admissions during a six-month period in 2012., Design: Glasgow Coma Scale and Full Outline of UnResponsiveness score were recorded within 1 hour of admission. Baseline characteristics and physiologic components of the Acute Physiology and Chronic Health Evaluation system, as well as mortality were linked to Glasgow Coma Scale/Full Outline of UnResponsiveness score information., Interventions: None., Measurements and Results: We recruited 1,695 critically ill patients, of which 1,645 with complete data could be linked to data in the Acute Physiology and Chronic Health Evaluation system. The area under the receiver operating characteristic curve of predicting ICU mortality using the Glasgow Coma Scale was 0.715 (95% CI, 0.663-0.768) and using the Full Outline of UnResponsiveness score was 0.742 (95% CI, 0.694-0.790), statistically different (p = 0.001). A similar but nonsignificant difference was found for predicting hospital mortality (p = 0.078). The respiratory and brainstem reflex components of the Full Outline of UnResponsiveness score showed a much wider range of mortality than the verbal component of Glasgow Coma Scale. In multivariable models, the Full Outline of UnResponsiveness score was more useful than the Glasgow Coma Scale for predicting mortality., Conclusions: The Full Outline of UnResponsiveness score might be a better prognostic tool of ICU mortality than the Glasgow Coma Scale in critically ill patients, most likely a result of incorporating brainstem reflexes and respiration into the Full Outline of UnResponsiveness score.

Objectives: To compare ICU performance using standardized mortality ratios generated by the Acute Physiology and Chronic Health Evaluation IVa and a National Quality Forum-endorsed methodology and examine potential reasons for model-based standardized mortality ratio differences., Design: Retrospective analysis of day 1 hospital mortality predictions at the ICU level using Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum models on the same patient cohort., Setting: Forty-seven ICUs at 36 U.S. hospitals from January 2008 to May 2013., Patients: Eighty-nine thousand three hundred fifty-three consecutive unselected ICU admissions., Interventions: None., Measurements and Main Results: We assessed standardized mortality ratios for each ICU using data for patients eligible for Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum predictions in order to compare unit-level model performance, differences in ICU rankings, and how case-mix adjustment might explain standardized mortality ratio differences. Hospital mortality was 11.5%. Overall standardized mortality ratio was 0.89 using Acute Physiology and Chronic Health Evaluation IVa and 1.07 using National Quality Forum, the latter having a widely dispersed and multimodal standardized mortality ratio distribution. Model exclusion criteria eliminated mortality predictions for 10.6% of patients for Acute Physiology and Chronic Health Evaluation IVa and 27.9% for National Quality Forum. The two models agreed on the significance and direction of standardized mortality ratio only 45% of the time. Four ICUs had standardized mortality ratios significantly less than 1.0 using Acute Physiology and Chronic Health Evaluation IVa, but significantly greater than 1.0 using National Quality Forum. Two ICUs had standardized mortality ratios exceeding 1.75 using National Quality Forum, but nonsignificant performance using Acute Physiology and Chronic Health Evaluation IVa. Stratification by patient and institutional characteristics indicated that units caring for more severely ill patients and those with a higher percentage of patients on mechanical ventilation had the most discordant standardized mortality ratios between the two predictive models., Conclusions: Acute Physiology and Chronic Health Evaluation IVa and National Quality Forum models yield different ICU performance assessments due to differences in case-mix adjustment. Given the growing role of outcomes in driving prospective payment patient referral and public reporting, performance should be assessed by models with fewer exclusions, superior accuracy, and better case-mix adjustment.