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OBJECTIVE: : To examine the association between ICU readmission rates and case-mix-adjusted outcomes. DESIGN: : Retrospective cohort study of ICU admissions from 2002 to 2010. SETTING: : One hundred five ICUs at 46 United States hospitals. PATIENTS: : Of 369,129 admissions, 263,082 were first admissions that were alive at ICU discharge and candidates for readmission. INTERVENTIONS: : None. MEASUREMENTS AND MAIN RESULTS: : The median unit readmission rate was 5.9% (intraquartile range 5.1%-7.0%). Across all admissions, hospital mortality for patients with and without readmission was 21.3% vs. 3.6%, mean ICU stay 4.9 days vs. 3.4 days, and hospital stay 13.3 days vs. 4.5 days, respectively. We stratified ICUs according to their readmission rate: high (>7%), moderate (5%-7%), and low (<5%) rates. Observed and case-mix-adjusted hospital mortality, ICU and hospital lengths of stay were examined by readmission rate strata. Observed outcomes were much worse in the high readmission rate units. But after adjusting for patient and institutional differences, there was no association between level of unit readmission rate and case-mix-adjusted mortality. The difference between observed and predicted mortality was -0.4%, 0.4%, and -1.1%, for the high, medium, and low readmission rate strata, respectively. Additionally, the difference between observed and expected ICU length of stay was approximately zero for the three strata. CONCLUSIONS: : Patients readmitted to ICUs have increased hospital mortality and lengths of stay. After case-mix adjustment, there were no significant differences in standardized mortality or case-mix-adjusted lengths of stay between units with high readmission rates compared to units with moderate or low rates. The use of readmission as a quality measure should only be implemented if patient case-mix is taken into account. [ABSTRACT FROM AUTHOR]

OBJECTIVE: : To examine which patient characteristics increase the risk for intensive care unit readmission and assess the association of readmission with case-mix adjusted mortality and resource use. DESIGN: : Retrospective cohort study. SETTING: : Ninety-seven intensive and cardiac care units at 35 hospitals in the United States. PATIENTS: : A total of 229,375 initial intensive care unit admissions during 2001 through 2009 who met inclusion criteria. INTERVENTIONS: : None. MEASUREMENTS AND MAIN RESULTS: : For patients who were discharged alive and candidates for readmission, we compared the characteristics of those with and without a readmission. A multivariable logistic regression analysis was used to identify potential patient-level characteristics that increase the risk for subsequent readmission. We also evaluated case-mix adjusted outcomes by comparing observed and predicted values of mortality and length of stay for patients with and without intensive care unit readmission. Among 229,375 first admissions that met inclusion criteria, 13,980 (6.1%) were eventually readmitted. Risk factors associated with the highest multivariate odds ratio for unit readmission included location before intensive care unit admission, age, comorbid conditions, diagnosis, intensive care unit length of stay, physiologic abnormalities at intensive care discharge, and discharge to a step-down unit. After adjustment for risk factors, patients who were readmitted had a four-fold greater probability of hospital mortality and a 2.5-fold increase in hospital stay compared to patients without readmission. CONCLUSIONS: : Intensive care readmission is associated with patient factors that reflect a greater severity and complexity of illness, resulting in a higher risk for hospital mortality and a longer hospital stay. To improve patient safety, physicians should consider these risk factors when making intensive care discharge decisions. Because intensive care unit readmission correlates with more complex and severe illness, readmission rates require case-mix adjustment before they can be properly interpreted as quality measures. [ABSTRACT FROM AUTHOR]

Background: Hospital performance measures rely on aggregate outcomes. For patients receiving mechanical ventilation (MV), outcomes depend on severity of illness, hospital MV volume, and case mix. Patients requiring prolonged acute MV (PAMV) [MV for >or= 96 h] comprise a resource-intensive group, but the impact of its volume on aggregate outcomes is unknown. We investigated whether observed outcomes differed from those predicted by APACHE (acute physiology and chronic health evaluation) IV risk adjustment and the relationship between hospital MV volume and outcomes among patients receiving PAMV.Methods: We conducted a retrospective cohort study using the APACHE IV database between the years 2001 and 2005.Results: Of the 94,553 patients receiving MV at 45 hospitals, 24,366 (25.8%) were receiving PAMV. Unadjusted mortality was 32.3% for patients receiving PAMV and 22.9% for patients receiving short-term MV (STMV) [< 96 h]. Although mortality predictions were accurate in both groups, the length-of-stay (LOS) predictions underestimated duration of MV, ICU LOS, and hospital LOS by 5.2, 4.6, and 5.4 days, respectively, in the PAMV group. After stratifying the PAMV group by hospital MV volume, except for quintile 1, the standardized mortality ratio (SMR) was found to be inversely related to the volume quintile. The difference between actual and predicted MV durations, however, exhibited a consistent direct relationship with the MV volume.Conclusions: In patients requiring PAMV, the SMR is inversely proportional to hospital MV volume. Conversely, the PAMV group had a disproportionate effect on durations of MV, ICU LOS, and hospital LOS, and these marginal excesses increased with the hospital MV volume quintile. Development of specific predictive equations for patients receiving PAMV is recommended. Benchmarking measures must consider the case mix of patients receiving STMV vs those receiving PAMV. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To validate performance characteristics of the intensive care unit (ICU) admission mortality probability model, version III (MPM0-III) on Project IMPACT data submitted in 2004 and 2005. This data set was external from the MPM0-III developmental and internal validation data collected between 2001 and 2004. DESIGN: Retrospective analysis of clinical data collected concurrently with care. SETTING: One hundred three (103) adult ICUs in North America that voluntarily collect and submit data to Project IMPACT. SUBJECTS: A total of 55,459 patients who were eligible for MPM scoring (age >or=18; first ICU admission for hospitalization, excludes burns, coronary care, and cardiac surgical patients). INTERVENTIONS: None. MEASUREMENTS: Prevalence of MPM risk factors and their relationship to hospital mortality; calibration and discrimination of MPM0-III model applied to new data. MAIN RESULTS: Seventy-eight ICUs contributed data to both this study and the original development set. Fifty-six ICUs from the original MPM0-III study were replaced by 25 new ICUs in this external validation set. Patient characteristics (type of patient, risk factors, and resuscitation status) were similar to the original 2001-2004 cohort, except for slightly more patients on mechanical ventilation at admission (32% vs. 27%, p < 0.01) and the percentage of patients having no MPM0-III risk factors except age (11% vs. 14%, p < 0.01). Observed deaths were 7331 (13.2%) vs. 7456 predicted, yielding a standardized mortality ratio of 0.983, 95% CI (0.963-1.001). CONCLUSIONS: MPM0-III calibrates on a new population of 55,459 North American patients who include many patients from new ICUs, which helps confirm that the model is robust and was not overfitted to the development sample. Although Project IMPACT participants change over time, 2004-2005 patient risk factors and their relationship to hospital mortality have not significantly changed. The increase in mechanically ventilated patients and reduction in admissions with no risk factors are trends worth following. [ABSTRACT FROM AUTHOR]

RATIONALE: General intensive care units (ICUs) provide care across a wide range of diagnoses, whereas specialty ICUs provide diagnosis-specific care. Risk-adjusted outcome differences across such units are unknown. OBJECTIVES: To determine the association between specialty ICU care and the outcome of critical illness. METHODS: We conducted a retrospective cohort study design analyzing patients admitted to 124 ICUs participating in the Acute Physiology and Chronic Health Evaluation IV from January 2002 to December 2005. We examined 84,182 patients admitted to specialty and general ICUs with an admitting diagnosis or procedure of acute coronary syndrome, ischemic stroke, intracranial hemorrhage, pneumonia, abdominal surgery, or coronary-artery bypass graft surgery. ICU type was determined by a local data coordinator at each site. Patients were classified by admission to a general ICU, a diagnosis-appropriate ('ideal') specialty ICU, or a diagnosis-inappropriate ('non-ideal') specialty ICU. The primary outcomes were in-hospital mortality and ICU length of stay. MEASUREMENTS AND MAIN RESULTS: After adjusting for important confounders, there were no significant differences in risk-adjusted mortality between general versus ideal specialty ICUs for all conditions other than pneumonia. Risk-adjusted mortality was significantly greater for patients admitted to non-ideal specialty ICUs. There was no consistent effect of specialization on length of stay for all patients or for ICU survivors. CONCLUSIONS: Ideal specialty ICU care appears to offer no survival benefit over general ICU care for select common diagnoses. Non-ideal specialty ICU care (i.e., 'boarding') is associated with increased risk-adjusted mortality. [ABSTRACT FROM AUTHOR]

OBJECTIVE:: To examine the Hosmer-Lemeshow test's sensitivity in evaluating the calibration of models predicting hospital mortality in large critical care populations. DESIGN:: Simulation study. SETTING:: Intensive care unit databases used for predictive modeling. PATIENTS:: Data sets were simulated representing the approximate number of patients used in earlier versions of critical care predictive models (n = 5,000 and 10,000) and more recent predictive models (n = 50,000). Each patient had a hospital mortality probability generated as a function of 23 risk variables. INTERVENTIONS:: None. MEASUREMENTS AND MAIN RESULTS:: Data sets of 5,000, 10,000, and 50,000 patients were replicated 1,000 times. Logistic regression models were evaluated for each simulated data set. This process was initially carried out under conditions of perfect fit (observed mortality = predicted mortality; standardized mortality ratio = 1.000) and repeated with an observed mortality that differed slightly (0.4%) from predicted mortality. Under conditions of perfect fit, the Hosmer-Lemeshow test was not influenced by the number of patients in the data set. In situations where there was a slight deviation from perfect fit, the Hosmer-Lemeshow test was sensitive to sample size. For populations of 5,000 patients, 10% of the Hosmer-Lemeshow tests were significant at p < .05, whereas for 10,000 patients 34% of the Hosmer-Lemeshow tests were significant at p < .05. When the number of patients matched contemporary studies (i.e., 50,000 patients), the Hosmer-Lemeshow test was statistically significant in 100% of the models. CONCLUSIONS:: Caution should be used in interpreting the calibration of predictive models developed using a smaller data set when applied to larger numbers of patients. A significant Hosmer-Lemeshow test does not necessarily mean that a predictive model is not useful or suspect. While decisions concerning a mortality model's suitability should include the Hosmer-Lemeshow test, additional information needs to be taken into consideration. This includes the overall number of patients, the observed and predicted probabilities within each decile, and adjunct measures of model calibration. [ABSTRACT FROM AUTHOR]

BACKGROUND: Transferring critically ill patients to other acute care hospitals may artificially impact benchmarking measures. We sought to quantify the effect of out-of-hospital transfers on the standardized mortality ratio (SMR), an outcome-based measure of ICU performance. METHODS: We performed a cohort study and Monte Carlo simulation using data from 85 ICUs participating in the acute physiology and chronic health evaluation (APACHE) clinical information system from 2002 to 2003. The SMR (observed divided by expected hospital mortality) was calculated for each ICU using APACHE IV risk adjustment. A set number of patients was randomly assigned to be transferred out alive rather than experience their original outcome. The SMR was recalculated, and the mean simulated SMR was compared to the original. RESULTS: The mean (+/- SD) baseline SMR was 1.06 +/- 0.19. In the simulation, increasing the number of transfers by 2% and 6% over baseline decreased the SMR by 0.10 +/- 0.03 and 0.14 +/- 0.03, respectively. At a 2% increase, 27 ICUs had a decrease in SMR of > 0.10, and two ICUs had a decrease in SMR of > 0.20. Transferring only one additional patient per month was enough to create a bias of > 0.1 in 27 ICUs. CONCLUSIONS: Increasing the number of acute care transfers by a small amount can significantly bias the SMR, leading to incorrect inference about ICU quality. Sensitivity to the variation in hospital discharge practices greatly limits the use of the SMR as a quality measure. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To revise and update the Acute Physiology and Chronic Health Evaluation (APACHE) model for predicting intensive care unit (ICU) length of stay. DESIGN: Observational cohort study. SETTING: One hundred and four ICUs in 45 U.S. hospitals. PATIENTS: Patients included 131,618 consecutive ICU admissions during 2002 and 2003, of which 116,209 met inclusion criteria. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The APACHE IV model for predicting ICU length of stay was developed using ICU day 1 patient data and a multivariate linear regression procedure to estimate the precise ICU stay for randomly selected patients who comprised 60% of the database. New variables were added to the previous APACHE III model, and advanced statistical modeling techniques were used. Accuracy was assessed by comparing mean observed and mean predicted ICU stay for the excluded 40% of patients. Aggregate mean observed ICU stay was 3.86 days and mean predicted 3.78 days (p < .001), a difference of 1.9 hrs. For 108 (93%) of 116 diagnoses, there was no significant difference between mean observed and mean predicted ICU stay. The model accounted for 21.5% of the variation in ICU stay across individual patients and 62% across ICUs. Correspondence between mean observed and mean predicted length of stay was reduced for patients with a short (< or =1.7 days) or long (> or =9.4 days) ICU stay and a low (<20%) or high (>80%) risk of death on ICU day 1. CONCLUSIONS: The APACHE IV model provides clinically useful ICU length of stay predictions for critically ill patient groups, but its accuracy and utility are limited for individual patients. APACHE IV benchmarks for ICU stay are useful for assessing the efficiency of unit throughput and support examination of structural, managerial, and patient factors that affect ICU stay. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To improve the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) method for predicting hospital mortality among critically ill adults and to evaluate changes in the accuracy of earlier APACHE models. DESIGN:: Observational cohort study. SETTING: A total of 104 intensive care units (ICUs) in 45 U.S. hospitals. PATIENTS: A total of 131,618 consecutive ICU admissions during 2002 and 2003, of which 110,558 met inclusion criteria and had complete data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We developed APACHE IV using ICU day 1 information and a multivariate logistic regression procedure to estimate the probability of hospital death for randomly selected patients who comprised 60% of the database. Predictor variables were similar to those in APACHE III, but new variables were added and different statistical modeling used. We assessed the accuracy of APACHE IV predictions by comparing observed and predicted hospital mortality for the excluded patients (validation set). We tested discrimination and used multiple tests of calibration in aggregate and for patient subgroups. APACHE IV had good discrimination (area under the receiver operating characteristic curve = 0.88) and calibration (Hosmer-Lemeshow C statistic = 16.9, p = .08). For 90% of 116 ICU admission diagnoses, the ratio of observed to predicted mortality was not significantly different from 1.0. We also used the validation data set to compare the accuracy of APACHE IV predictions to those using APACHE III versions developed 7 and 14 yrs previously. There was little change in discrimination, but aggregate mortality was systematically overestimated as model age increased. When examined across disease, predictive accuracy was maintained for some diagnoses but for others seemed to reflect changes in practice or therapy. CONCLUSIONS: APACHE IV predictions of hospital mortality have good discrimination and calibration and should be useful for benchmarking performance in U.S. ICUs. The accuracy of predictive models is dynamic and should be periodically retested. When accuracy deteriorates they should be revised and updated. [ABSTRACT FROM AUTHOR]

Background: An increased volume of patients is associated with improved survival in numerous high-risk medical and surgical conditions. The relationship between the number of patients admitted (hospital volume) and outcome among patients with critical illnesses is unknown.Methods: We analyzed data from 20,241 nonsurgical patients receiving mechanical ventilation at 37 acute care hospitals in the Acute Physiology and Chronic Health Evaluation clinical information system from 2002 through 2003. Multivariate analyses were performed to adjust for the severity of illness and other differences in the case mix.Results: An increase in hospital volume was associated with improved survival among patients receiving mechanical ventilation in the intensive care unit (ICU) and in the hospital. Admission to a hospital in the highest quartile according to volume (i.e., >400 patients receiving mechanical ventilation per year) was associated with a 37 percent reduction in the adjusted odds of death in the ICU as compared with admission to hospitals in the lowest quartile (< or =150 patients receiving mechanical ventilation per year, P<0.001). In-hospital mortality was similarly reduced (adjusted odds ratio, 0.66; 95 percent confidence interval, 0.52 to 0.83; P<0.001). A typical patient in a hospital in a low-volume quartile would have an adjusted in-hospital mortality of 34.2 percent as compared with 25.5 percent in a hospital in a high-volume quartile. Among survivors, there were no significant trends in the length of stay in the ICU or the hospital.Conclusions: Mechanical ventilation of patients in a hospital with a high case volume is associated with reduced mortality. Further research is needed to determine the mechanism of the relationship between volume and outcome among patients with a critical illness. [ABSTRACT FROM AUTHOR]

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.

Purpose of Review: There are few first-hand accounts that describe the history of outcome prediction in critical care. This review summarizes the authors' personal perspectives about the development and evolution of Acute Physiology and Chronic Health Evaluation over the past 35 years., Recent Findings: We emphasize what we have learned in the past and more recently our perspectives about the current status of outcome prediction, and speculate about the future of outcome prediction., Summary: There is increasing evidence that superior accuracy in outcome prediction requires complex modeling with detailed adjustment for diagnosis and physiologic abnormalities. Thus, an automated electronic system is recommended for gathering data and generating predictions. Support, either public or private, is required to assist users and to update and improve models. Current outcome prediction models have increasingly focused on benchmarks for resource use, a trend that seems likely to increase in the future.

Objective: To examine the accuracy of the original Mortality Probability Admission Model III, ICU Outcomes Model/National Quality Forum modification of Mortality Probability Admission Model III, and Acute Physiology and Chronic Health Evaluation IVa models for comparing observed and risk-adjusted hospital mortality predictions., Design: Retrospective paired analyses of day 1 hospital mortality predictions using three prognostic models., Setting: Fifty-five ICUs at 38 U.S. hospitals from January 2008 to December 2012., Patients: Among 174,001 intensive care admissions, 109,926 met model inclusion criteria and 55,304 had data for mortality prediction using all three models., Interventions: None., Measurements and Main Results: We compared patient exclusions and the discrimination, calibration, and accuracy for each model. Acute Physiology and Chronic Health Evaluation IVa excluded 10.7% of all patients, ICU Outcomes Model/National Quality Forum 20.1%, and Mortality Probability Admission Model III 24.1%. Discrimination of Acute Physiology and Chronic Health Evaluation IVa was superior with area under receiver operating curve (0.88) compared with Mortality Probability Admission Model III (0.81) and ICU Outcomes Model/National Quality Forum (0.80). Acute Physiology and Chronic Health Evaluation IVa was better calibrated (lowest Hosmer-Lemeshow statistic). The accuracy of Acute Physiology and Chronic Health Evaluation IVa was superior (adjusted Brier score = 31.0%) to that for Mortality Probability Admission Model III (16.1%) and ICU Outcomes Model/National Quality Forum (17.8%). Compared with observed mortality, Acute Physiology and Chronic Health Evaluation IVa overpredicted mortality by 1.5% and Mortality Probability Admission Model III by 3.1%; ICU Outcomes Model/National Quality Forum underpredicted mortality by 1.2%. Calibration curves showed that Acute Physiology and Chronic Health Evaluation performed well over the entire risk range, unlike the Mortality Probability Admission Model and ICU Outcomes Model/National Quality Forum models. Acute Physiology and Chronic Health Evaluation IVa had better accuracy within patient subgroups and for specific admission diagnoses., Conclusions: Acute Physiology and Chronic Health Evaluation IVa offered the best discrimination and calibration on a large common dataset and excluded fewer patients than Mortality Probability Admission Model III or ICU Outcomes Model/National Quality Forum. The choice of ICU performance benchmarks should be based on a comparison of model accuracy using data for identical patients.

Objectives: Detailed data on occupancy and use of mechanical ventilators in U. S. ICU over time and across unit types are lacking. We sought to describe the hourly bed occupancy and use of ventilators in U.S. ICUs to improve future planning of both the routine and disaster provision of intensive care., Design: Retrospective cohort study. We calculated mean hourly bed occupancy in each ICU and hourly bed occupancy for patients on mechanical ventilators. We assessed trends in overall occupancy over the 3 years. We also assessed occupancy and mechanical ventilation rates across different types and sizes of ICUs., Setting: Ninety-seven U.S. ICUs participating in Project IMPACT from 2005 to 2007., Patients: A total of 226,942 consecutive admissions to ICUs., Interventions: None., Measurements and Main Results: Over the 3 years studied, total ICU occupancy ranged from 57.4% to 82.1% and the number of beds filled with mechanically ventilated patients ranged from 20.7% to 38.9%. There was no change in occupancy across years and no increase in occupancy during influenza seasons. Mean hourly occupancy across ICUs was 68.2% ± 21.3% (SD) and was substantially higher in ICUs with fewer beds (mean, 75.8% ± 16.5% for 5-14 beds vs 60.9% ± 22.1% for 20+ beds, p = 0.001) and in academic hospitals (78.7% ± 15.9% vs 65.3% ± 21.3% for community not-for-profit hospitals, p < 0.001). More than half of ICUs (53.6%) had 4+ beds available more than half the time. The mean percentage of ICU patients receiving mechanical ventilation in any given hour was 39.5% (± 15.2%), and a mean of 29.0% (± 15.9%) of ICU beds were filled with a patient on a ventilator., Conclusions: Occupancy of U.S. ICUs was stable over time, but there is uneven distribution across different types and sizes of units. Only three of 10 beds were filled at any time with mechanically ventilated patients, suggesting substantial surge capacity throughout the system to care for acutely critically ill patients.