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Objectives To develop a model to benchmark mortality in hospitalized patients using accessible electronic medical record data. Design Univariate analysis and multivariable logistic regression were used to identify variables collected during the first 24 hours following admission to test for risk factors associated with the end point of hospital mortality. Models were built using specific diagnosis (International Classification of Diseases, 9th Edition or International Classification of Diseases, 10th Edition) captured at discharge, rather than admission diagnosis, which may be discordant. Variables were selected based, in part, on prior the Acute Physiology and Chronic Health Evaluation methodology and included primary diagnosis information plus three aggregated indices (physiology, comorbidity, and support). A Physiology Index was created using parsimonious nonlinear modeling of heart rate, mean arterial pressure, temperature, respiratory rate, hematocrit, platelet counts, and serum sodium. A Comorbidity Index incorporates new or ongoing diagnoses captured by the electronic medical record during the preceding year. A Support Index considered 10 interventions such as mechanical ventilation, selected IV drugs, and hemodialysis. Accuracy was determined using area under the receiver operating curve for discrimination, calibration curves, and modified Brier score for calibration. Setting and patients We used deidentified electronic medical record data from 74,434 adult inpatients (ICU and ward) at 15 hospitals from 2010 to 2013 to develop the mortality model and validated using data for additional 49,752 patients from the same 15 hospitals. A second revalidation was accomplished using data on 83,684 patients receiving care at six hospitals between 2014 and 2016. The model was also validated on a subset of patients with an ICU stay on day 1. Interventions None. Measurements and main results This model uses physiology, comorbidity, and support indices, primary diagnosis, age, lowest Glasgow Coma Score, and elapsed time since hospital admission to predict hospital mortality. In the initial validation cohort, observed mortality was 4.04% versus predicted mortality 4.12% (Student t test, p = 0.37). In the revalidation using a different set of hospitals, predicted and observed mortality were 2.66% and 2.99%, respectively. Area under the receiver operating curve were 0.902 (0.895-0.909) and 0.884 (0.877-0.891), respectively, and calibration curves show a close relationship of observed and predicted mortalities. In the evaluation of the subset of ICU patients on day1, the area under the receiver operating curve was 0.87, with an observed mortality of 8.78% versus predicted mortality of 8.93% (Student t test, p = 0.52) and a standardized mortality ratio of 0.98 (0.932-1.034). Conclusions Variables considered by traditional ICU prognostic models accurately benchmark patient mortality for patients receiving care in multiple hospital locations, not only the ICU. Unlike Acute Physiology and Chronic Health Evaluation, this model relies on electronic medical record data alone and does not require personnel to collect the independent predictor variables. Assessing the model's utility for benchmarking hospital performance will require prospective testing in a larger representative sample of hospitals.

Objective: To derive and validate a model for risk of resistance to first-line community-acquired pneumonia (CAP) therapy. Design: We developed a logistic regression prediction model from a large multihospital discharge database and validated it versus the Drug Resistance in Pneumonia (DRIP) score in a holdout sample and another hospital system outside that database. Resistance to first-line CAP therapy (quinolone or third generation cephalosporin plus macrolide) was based on blood or respiratory cultures. Setting: This study was conducted using data from 177 Premier Healthcare database hospitals and 11 Cleveland Clinic hospitals. Participants: Adults hospitalized for CAP. Exposure: Risk factors for resistant infection. Results: Among 138,762 eligible patients in the Premier database, 12,181 (8.8%) had positive cultures and 5,200 (3.8%) had organisms resistant to CAP therapy. Infection with a resistant organism in the previous year was the strongest predictor of resistance; markers of acute illness (eg, receipt of mechanical ventilation or vasopressors) and chronic illness (eg, pressure ulcer, paralysis) were also associated with resistant infections. Our model outperformed the DRIP score with a C-statistic of 0.71 versus 0.63 for the DRIP score (P < .001) in the Premier holdout sample, and 0.65 versus 0.58 (P < .001) in Cleveland Clinic hospitals. Clinicians at Premier facilities used broad-spectrum antibiotics for 20%–30% of patients. In discriminating between patients with and without resistant infections, physician judgment slightly outperformed the DRIP instrument but not our model. Conclusions: Our model predicting infection with a resistant pathogen outperformed both the DRIP score and physician practice in an external validation set. Its integration into practice could reduce unnecessary use of broad-spectrum antibiotics.

Guidelines recommend testing hospitalized patients with community-acquired pneumonia (CAP) for Legionella pneumophila only if the infection is severe or risk factors are present. There are no validated models for predicting Legionella.To derive and externally validate a model to predict a positive Legionella test.Diagnostic study of adult inpatients with pneumonia using data from 177 US hospitals in the Premier Healthcare Database (training and hold-out validation sets) and 12 Cleveland Clinic Health System (CCHS) hospitals (external validation set). We used multiple logistic regression to predict positive Legionella tests in the training set, and evaluated performance in both validation sets.The outcome was a positive Legionella test. Potential predictors included demographics and co-morbidities, disease severity indicators, season, region, and presence of a local outbreak.Of 166,689 patients hospitalized for pneumonia, 43,070 were tested for Legionella and 642 (1.5%) tested positive. The strongest predictors of a positive test were a local outbreak (odds ratio [OR], 3.4), June-October occurrence (OR, 3.4), hyponatremia (OR, 3.3), smoking (OR, 2.4), and diarrhea (OR, 2.0); prior admission within 6 months (OR, 0.27) and chronic pulmonary disease (OR, 0.49) were associated with a negative test. Model c-statistics were 0.79 in the Premier and 0.77 in the CCHS validation samples. High-risk patients were only slightly more likely to have been tested than lower-risk patients. Compared to actual practice, the model-based testing strategy detected twice as many cases.Although Legionella is an uncommon cause of pneumonia, patient characteristics can identify individuals at high risk, allowing for more efficient testing.

Background Choice of empiric therapy for pneumonia depends on risk for antimicrobial resistance. Models to predict resistance are derived from blood and respiratory culture results. We compared these results to understand if organisms and resistance patterns differed by site. We also compared characteristics and outcomes of patients with positive cultures by site. Methods We studied adult patients discharged from 177 US hospitals from July 2010 through June 2015, with principal diagnoses of pneumonia, or principal diagnoses of respiratory failure, acute respiratory distress syndrome, respiratory arrest, or sepsis with a secondary diagnosis of pneumonia, and who had blood or respiratory cultures performed. Demographics, treatment, microbiologic results, and outcomes were examined. Results Among 138 561 hospitalizations of patients with pneumonia who had blood or respiratory cultures obtained at admission, 12 888 (9.3%) yielded positive cultures: 6438 respiratory cultures, 5992 blood cultures, and 458 both respiratory and blood cultures. Forty-two percent had isolates resistant to first-line therapy for community-acquired pneumonia. Isolates from respiratory samples were more often resistant than were isolates from blood (54.2% vs 26.6%; P < .001). Patients with both culture sites positive had higher case-fatality, longer lengths of stay, and higher costs than patients who had only blood or respiratory cultures positive. Among respiratory cultures, the most common pathogens were Staphylococcus aureus (34%) and Pseudomonas aeruginosa (17%), whereas blood cultures most commonly grew Streptococcus pneumoniae (33%), followed by S. aureus (22%). Conclusions Patients with positive respiratory tract cultures are clinically different from those with positive blood cultures, and resistance patterns differ by source. Models of antibiotic resistance should account for culture source.

BackgroundFor patients at risk for multidrug-resistant organisms, IDSA/ATS guidelines recommend empiric therapy against methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas. Following negative cultures, the guidelines recommend antimicrobial de-escalation. We assessed antibiotic de-escalation practices across hospitals and their associations with outcomes in hospitalized patients with pneumonia with negative cultures.MethodsWe included adults admitted with pneumonia in 2010–2015 to 164 US hospitals if they had negative blood and/or respiratory cultures and received both anti-MRSA and antipseudomonal agents other than quinolones. De-escalation was defined as stopping both empiric drugs on day 4 while continuing another antibiotic. Patients were propensity adjusted for de-escalation and compared on in-hospital 14-day mortality, late deterioration (ICU transfer), length-of-stay (LOS), and costs. We also compared adjusted outcomes across hospital de-escalation rate quartiles.ResultsOf 14 170 patients, 1924 (13%) had both initial empiric drugs stopped by hospital day 4. Hospital de-escalation rates ranged from 2–35% and hospital de-escalation rate quartile was not significantly associated with outcomes. At hospitals in the top quartile of de-escalation, even among patients at lowest risk for mortality, the de-escalation rates were ConclusionsA minority of eligible patients with pneumonia had antibiotics de-escalated by hospital day 4 following negative cultures and de-escalation rates varied widely between hospitals. To adhere to recent guidelines will require substantial changes in practice.

To examine the association between body mass index (BMI) and in-hospital mortality in patients presenting with Clostridium difficile infections in emergency department visits (ED) in the USA. Infected patients with extreme BMIs may have an elevated mortality risk, but prior studies examining this question have been too small to reach definitive conclusions. Data were from the Nationwide Emergency Department Sample (NEDS), Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality during 2012. NEDS records emergency department (ED) visits across the USA and provides statistical sampling weights to approximate a nationally representative sample of US hospital-based EDs. Inclusion criteria were adults age 18 or older with an ICD-9 code for C. difficile infection (008.45) and a documented body mass index ICD-9 V code (V85.x). Logistic regression was used to predict mortality after adjusting for demographic variables and chronic comorbidities defined by Elixhauser. A weighted sample of 22,937 ED visits met all inclusion criteria. The cohort’s mean age was 66. 64.6% were female. The unadjusted mortality rate was 6.5%. Patients with a BMI 40 kg/m2 were associated with significantly greater mortality risk. Being underweight (BMI 40) was associated with increased risk of in-hospital mortality in patients presenting with C. difficile infections.

BACKGROUND Pressure injuries, also known as pressure ulcers, are a serious complication of immobility. Patients should be thoroughly examined for pressure injuries when admitted to the intensive care unit to optimize treatment. Whether community-acquired pressure injuries correlate with poor hospital outcomes among critically ill patients is understudied. OBJECTIVES To determine whether pressure injuries present upon admission to the intensive care unit can serve as a predictive marker for longer hospitalization and increased mortality. METHODS This study retrospectively analyzed admissions of adult patients to a 24-bed medical-surgical intensive care unit in a large level I trauma center in the northeast United States from 2010 to 2012. The association of pressure injuries with mortality and length of stay was assessed, using multivariable logistic regression and generalized linear models, adjusted for age, comorbidities, Acute Physiology and Chronic Health Evaluation III score, and other patient characteristics. RESULTS Among 2723 patients, 180 (6.6%) had a pressure injury at admission. Patients with a pressure injury had longer mean unadjusted stay (15.6 vs 10.5 days; P < .001) and higher in-hospital mortality rate (32.2% vs 18.3%; P < .001) than did patients without a pressure injury at admission. After multivariable adjustment, pressure injuries were associated with a mean increase in length of stay of 3.1 days (95% CI 1.5-4.7; P < .001). Pressure injuries were not associated with mortality after adjusting for the Acute Physiology and Chronic Health Evaluation III score, but they may serve as a marker for increased risk of mortality if an Acute Physiology and Chronic Health Evaluation III score is unavailable. CONCLUSION Pressure injuries present at admission to the intensive care unit are an objective, easy-to-identify finding associated with longer stays. Pressure injuries might have a modest association with higher risk of mortality.

INTRODUCTION: Predictions of ICU outcome vary by diagnosis, even when adjusted for age, physiology and chronic health We investigated APACHE-IVb adjusted outcomes in adult patients with ICU admission for novel coronavirus versus other viral pneumonias (VP) with and without ARDS METHODS: De-identified patient data from 132 hospitals in the US contributing data on VP patients to the APACHE database between 1/1/2014 and 12/31/2019 was compared with 44 hospitals contributing COVID-19 (C19) data between 3/14 and 6/17/20 RESULTS: 4200 pts had a primary (n=2544) or secondary (n=1666) ICU admitting diagnosis of viral pneumonia and 1494 had C19 infection Mean age was 64 1 for VP vs 63 4 for COVID (nsd) APACHEIII/IV scores were similar at 55 0 and 56 7 but gender and ethnic distributions differed with fewer females and Caucasians having C19 PaO2 / FiO2 ratio and WBC count at admission were lower with C19 Standardized APACHE IVb Mortality (95% CI) was 0 98 (0 88-1 09) for VP versus 1 52 (1 36-1 69) for C19 ICU (8 vs 5d) and hospital length of stay (13 vs 10d) were significantly longer with C19 patients, who had less IPPV or NIV support (47 vs 65%) but had more ventilator days (10 vs 4) A subgroup of VP patients with concurrent ARDS (n=202) were most similar to the C19 patients, with longer ICU LOS (13 d) and high mortality (21 6%), but SMR in this group was 0 89 (0 61-1 16) CONCLUSIONS: Severity-adjusted mortality and LOS are higher for C19 patients than for VP patients admitted to ICU C19 patients also have longer ventilator days and LOS, comparable to a subset of VP patients with concurrent ARDS APACHE-IVb under-predicts mortality across all levels of risk with C19 Admission APS/APACHE III/IV scores are similar, but pre-ICU LOS is longer Mortality and LOS increase with age and higher scores in both groups, but observed to predicted mortality and LOS are higher than expected with C19 patients These findings have implications for benchmarking ICU outcomes during the COVID-19 pandemic

Purpose This study compared the performance of 3 admission prognostic scores in predicting hospital mortality. Materials and methods Patient admission characteristics and hospital outcome of 9549 patients were recorded prospectively. The discrimination and calibration of the predicted risks of death derived from the Simplified Acute Physiology Score (SAPS III), Admission Mortality Prediction Model (MPM 0 III), and admission Acute Physiology and Chronic Health Evaluation (APACHE) II were assessed by the area under the receiver operating characteristic curve and a calibration plot, respectively. Measurements and main results Of the 9549 patients included in the study, 1276 patients (13.3%) died after intensive care unit admission. Patient admission characteristics were significantly different between the survivors and nonsurvivors. All 3 prognostic scores had a reasonable ability to discriminate between the survivors and nonsurvivors (area under the receiver operating characteristic curve for SAPS III, 0.836; MPM 0 III, 0.807; admission APACHE, 0.845), with best discrimination in emergency admissions. The SAPS III model had a slightly better calibration and overall performance (slope of calibration curve, 1.03; Brier score, 0.09; Nagelkerke R 2 , 0.297) compared to the MPM 0 III and admission APACHE II model. Conclusions All 3 intensive care unit admission prognostic scores had a good ability to predict hospital mortality of critically ill patients, with best discrimination in emergency admissions.

To assess how homelessness impacts mortality and length of stay (LOS) among select the intensive care unit (ICU) patients.We used ICD-9 code V60.0 to identify homeless patients using the Premier Perspective Database from January 2010 to June 2011. We identified three subpopulations who received critical care services using ICD-9 and Medicare Severity Diagnosis Related Groups (MS-DRG) codes: patients with a diagnosis of sepsis who were treated with antibiotics by Day 2, patients with an alcohol or drug related MS-DRG, and patients with a diabetes related MS-DRG. We used multivariable logistic regression to predict mortality and multivariable generalized estimating equations to predict hospital and ICU LOS.781,540 hospitalizations met inclusion criteria; 2278 (0.3%) were homeless. We found homelessness had no significant adjusted association with mortality among sepsis patients, but was associated with substantially longer hospital LOS: (3.7 days longer; 95% CI (1.7, 5.7, p .001). LOS did not differ in the Diabetes or Alcohol and Drug related DRG groups.Critically ill homeless patients with sepsis had longer hospital LOS but similar ICU LOS and mortality risk compared to non-homeless patients. Homelessness was not associated with increased LOS in the diabetes or alcohol and drug related groups.

OBJECTIVES To compare the characteristics and hospital outcomes of patients with an acute exacerbation of chronic obstructive pulmonary disease treated in the ICU with initial noninvasive ventilation or invasive mechanical ventilation. DESIGN Retrospective, multicenter cohort study of prospectively collected data. We used propensity matching to compare the outcomes of patients treated with noninvasive ventilation to those treated with invasive mechanical ventilation. We also assessed predictors for noninvasive ventilation failure. SETTING Thirty-eight hospitals participating in the Acute Physiology and Chronic Health Evaluation database from 2008 through 2012. SUBJECTS A total of 3,520 patients with a diagnosis of chronic obstructive pulmonary disease exacerbation including 27.7% who received noninvasive ventilation and 45.5% who received invasive mechanical ventilation. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Noninvasive ventilation failure was recorded in 13.7% from patients ventilated noninvasively. Hospital mortality was 7.4% for patients treated with noninvasive ventilation; 16.1% for those treated with invasive mechanical ventilation; and 22.5% for those who failed noninvasive ventilation. In the propensity-matched analysis, patients initially treated with noninvasive ventilation had a 41% lower risk of death compared with those treated with invasive mechanical ventilation (relative risk, 0.59; 95% CI, 0.36-0.97). Factors that were independently associated with noninvasive ventilation failure were Simplified Acute Physiology Score II (relative risk = 1.04 per point increase; 95% CI, 1.03-1.04) and the presence of cancer (2.29; 95% CI, 0.96-5.45). CONCLUSIONS Among critically ill adults with chronic obstructive pulmonary disease exacerbation, the receipt of noninvasive ventilation was associated with a lower risk of in-hospital mortality compared with that of invasive mechanical ventilation; noninvasive ventilation failure was associated with the worst outcomes. These results support the use of noninvasive ventilation as a first-line therapy in appropriately selected critically ill patients with chronic obstructive pulmonary disease while also highlighting the risks associated with noninvasive ventilation failure and the need to be cautious in the face of severe disease.

In the chronic care model, a missed appointment decreases continuity, adversely affects practice efficiency, and can harm quality of care. The aim of this study was to identify predictors of a missed appointment and develop a model to predict an individual's likelihood of missing an appointment. The research team performed a retrospective study in an urban, academic, underserved outpatient internal medicine clinic from January 2008 to June 2011. A missed appointment was defined as either a "no-show" or cancellation within 24 hours of the appointment time. Both patient and visit variables were considered. The patient population was randomly divided into derivation and validation sets (70/30). A logistic model from the derivation set was applied in the validation set. During the period of study, 11,546 patients generated 163,554 encounters; 45% of appointments in the derivation sample were missed. In the logistic model, percent previously missed appointments, wait time from booking to appointment, season, day of the week, provider type, and patient age, sex, and language proficiency were all associated with a missed appointment. The strongest predictors were percentage of previously missed appointments and wait time. Older age and non-English proficiency both decreased the likelihood of missing an appointment. In the validation set, the model had a c-statistic of 0.71, and showed no gross lack of fit (P=0.63), indicating acceptable calibration. A simple risk factor model can assist in predicting the likelihood that an individual patient will miss an appointment.

BACKGROUND The American Thoracic Society and Infectious Diseases Society of America guidelines for management of healthcare-associated pneumonia (HCAP), first published in 2005, have been controversial regarding the selection of empiric broad-spectrum antibiotics, whether the criteria for HCAP predicts the likelihood of infection with multidrug resistant organisms, and whether HCAP patients have improved outcomes when treated with empiric broad-spectrum antibiotics. METHODS A retrospective cohort study at 488 US hospitals from July 2007 to November 2011. Patients who met criteria for HCAP were included. Guideline-concordant antibiotics were assessed based on guideline recommendations. We assessed changes in hospital rates of concordant antibiotic use over time and their correlation with outcomes. RESULTS Among 149,963 patients with HCAP, 19.6% received fully guideline-concordant antibiotics, 21.7% received partially concordant antibiotics, and 58.9% received discordant antibiotics. Guideline concordance increased over time. Rates of fully or partially concordant antibiotics varied across hospitals (median 36.4%; interquartile range 25.8%-49.1%). Among patients who received discordant antibiotics, 81.5% were treated according to community-acquired pneumonia (CAP) guidelines. On average, the rate of guideline concordance increased by 2.2% per 6-month interval, while hospital level rates of mortality, excess length of stay, and progression to respiratory failure did not change. CONCLUSIONS In this large, nationally representative cohort, only 1 in 5 patients with risk factors for HCAP received treatment that was fully in accordance with guidelines, and many received CAP therapy instead. At the hospital level, increases in the use of concordant antibiotics were not associated with declines in mortality, excess length of stay, or progression to respiratory failure.

OBJECTIVE To examine the impact of hospital-onset Clostridium difficile infection (HOCDI) on the outcomes of patients with sepsis. BACKGROUND Most prior studies that have addressed this issue lacked adequate matching to controls, suffered from small sample size, or failed to consider time to infection. DESIGN Retrospective cohort study. SETTING AND PATIENTS We identified adults with a principal or secondary diagnosis of sepsis who received care at 1 of the institutions that participated in a large multihospital database between July 1, 2004 and December 31, 2010. Among eligible patients with sepsis, we identified patients who developed HOCDI during their hospital stay. MEASUREMENTS We used propensity matching and date of diagnosis to match cases to patients without Clostridium difficile infections and compared outcomes between the 2 groups. MAIN RESULTS Of 218,915 sepsis patients, 2368 (1.08%) developed HOCDI. Unadjusted in-hospital mortality was significantly higher in HOCDI patients than controls (25% vs 10%, P

Key Points Question What is the etiology of pneumonia among patients with alcohol use disorder, and is alcohol use disorder associated with poorer outcomes? Findings In this cohort study of 137 496 patients with pneumonia, the most common cause of pneumonia among patients with alcohol use disorder was Streptococcus pneumoniae; resistant gram-negative infections were rare. In comorbidity-adjusted models, alcohol use disorder was not significantly associated with inpatient mortality, but patients with alcohol use disorder undergoing alcohol withdrawal more frequently required late mechanical ventilation, vasopressors, and intensive care unit admissions and had increased lengths of stay and hospital costs. Meaning This study suggests that alcohol use disorder alone is not an independent risk factor for resistant infection or mortality, but alcohol withdrawal is associated with clinical deterioration and higher use of health care resources., Importance Patients with alcohol use disorder (AUD) are at elevated risk of developing pneumonia, but few studies have assessed the outcomes of pneumonia in patients with AUD. Objectives To compare the causes, treatment, and outcomes of pneumonia in patients with and without AUD and to understand the associations of comorbid illnesses, alcohol withdrawal, and any residual effects due to alcohol itself with patient outcomes. Design, Setting, and Participants A retrospective cohort study was conducted of 137 496 patients 18 years or older with pneumonia who were admitted to 177 US hospitals participating in the Premier Healthcare Database from July 1, 2010, to June 30, 2015. Statistical analysis was conducted from October 27, 2017, to August 20, 2018. Exposure Alcohol use disorders identified from International Classification of Diseases, Ninth Revision, Clinical Modification codes. Main Outcomes and Measures Pneumonia cause, antibiotic treatment, inpatient mortality, clinical deterioration, length of stay, and cost. Associations of AUD with these variables were studied using generalized linear mixed models. Results Of 137 496 patients with community-acquired pneumonia (70 358 women and 67 138 men; mean [SD] age, 69.5 [16.2] years), 3.5% had an AUD. Patients with an AUD were younger than those without an AUD (median age, 58.0 vs 73.0 years; P, This cohort study uses data from the Premier Healthcare Database to compare the causes, treatment, and outcomes in patients hopitalized with community-acquired pneumonia with and without alcohol use disorder.

Objectives/Hypothesis To systematically review existing literature on the effectiveness of medical management of chronic rhinosinusitis (CRS) in cystic fibrosis (CF) patients. Study Design Systematic review. Methods We performed a literature search of PubMed, Embase, and Cochrane CENTRAL from 1987 to 2012. Inclusion criteria included English language as containing original data, with five or more subjects, measurable clinical outcomes, and readily available interventions. Data were systematically collected on study design, patient demographics, clinical characteristics and outcomes, and level of evidence. Two investigators independently reviewed all manuscripts and performed a comprehensive quality assessment. Results Of 415 abstracts identified, 12 articles were included. These 12 studies reported on 701 adult and pediatric CF patients who underwent medical therapy. Medical treatment included antibiotics (4/12), topical steroids (4/12), dornase alfa (3/12), and ibuprofen (1/12). Outcome measures included symptom scores (7/12), endoscopic findings (7/12), radiographic findings (4/12), pulmonary function testing (4/12), and rhinomanometry (2/12). Most studies found improvement in at least one of the outcome measures. There was statistical significance in clinical outcomes with dornase alfa, beclomethasone, and betamethasone. Most studies were level 3 or 4 evidence (9/12), but three studies were level 1 or 2 evidence (two dornase alfa studies, one betamethasone study). Conclusions Dornase alfa and, to a lesser extent, topical steroids demonstrated significant benefits in the medical treatment CRS in CF. There was a lack of evidence to support antibiotic therapy in the outcomes assessed. Further high-quality studies should be carried out to determine the efficacy of various medical therapies for CRS in CF. Level of Evidence NA Laryngoscope, 124:1308–1313, 2014

BACKGROUND Fluoroquinolones have equivalent oral and intravenous bioavailability, but hospitalized patients with community-acquired pneumonia (CAP) generally are treated intravenously. Our objectives were to compare outcomes of hospitalized CAP patients initially receiving intravenous vs oral respiratory fluoroquinolones. METHODS This was a retrospective cohort study utilizing data from 340 hospitals involving CAP patients admitted to a non-intensive care unit (ICU) setting from 2007 to 2010, who received intravenous or oral levofloxacin or moxifloxacin. The primary outcome was in-hospital mortality. Secondary outcomes included clinical deterioration (transfer to ICU, initiation of vasopressors, or invasive mechanical ventilation [IMV] initiated after the second hospital day), antibiotic escalation, length of stay (LOS), and cost. RESULTS Of 36 405 patients who met inclusion criteria, 34 200 (94%) initially received intravenous treatment and 2205 (6%) received oral treatment. Patients who received oral fluoroquinolones had lower unadjusted mortality (1.4% vs 2.5%; P = .002), and shorter mean LOS (5.0 vs 5.3; P < .001). Multivariable models using stabilized inverse propensity treatment weighting revealed lower rates of antibiotic escalation for oral vs intravenous therapy (odds ratio [OR], 0.84; 95% confidence interval [CI], .74-.96) but no differences in hospital mortality (OR, 0.82; 95% CI, .58-1.15), LOS (difference in days 0.03; 95% CI, -.09-.15), cost (difference in $-7.7; 95% CI, -197.4-182.0), late ICU admission (OR, 1.04; 95% CI, .80-1.36), late IMV (OR, 1.17; 95% CI, .87-1.56), or late vasopressor use (OR, 0.94; 95% CI, .68-1.30). CONCLUSIONS Among hospitalized patients who received fluoroquinolones for CAP, there was no association between initial route of administration and outcomes. More patients may be treated orally without worsening outcomes.

It is unknown how the prevalence of hospitals with palliative care programs (PCPs) at the state level in the United States correlates with the treatment of critically ill patients.We examined the relationship between state-level PCP prevalence and commonly used treatments for critically ill patients as well as other public health metrics.We compiled state-level data for the year 2011 from multiple published sources. These included the poverty rate from the U.S. Census, public health measures such as the number of primary care physicians per 100,000 persons from America's Health Ranking website, and state-level rates for a series of validated ICD-9 (International Classification of Diseases, 9th Revision) procedure codes used for critically ill patients (e.g., prolonged acute mechanical ventilation [PAMV]) from the State Inpatient Databases (SID), Healthcare Cost and Utilization Project (HCUP), and Agency for Healthcare Research and Quality. State-level percentages of PCPs came from a published report by the Center to Advance Palliative Care (CAPC). We used the Kruskal-Wallis test and Pearson's correlation coefficient for statistical inference.State-level poverty rates were negatively correlated with the percent of hospitals with PCPs: r = -0.39, p = 0.005. States with more hospital-based PCPs had significantly lower rates of PAMV, tracheostomies, and hemodialysis but higher rates of nutritional support than states with fewer PCPs.States with more poverty and/or at high risk for delivering inefficient health care had fewer hospital PCPs. Hospital-based PCPs may influence the frequency of some interventions for critically ill patients.

Purpose of review This review discusses the importance of intravenous fluid dose and composition in surgical ICU patients. On the basis of updated physiologic postulates, we suggest guidelines for the use of crystalloids and colloids. Goal-directed fluid therapy is advocated as a means for avoiding both hypovolemia and hypervolemia. Recent findings Integrity of the endothelial surface layer (ESL) and 'volume context' are key determinants of fluid disposition. During critical illness the ESL is compromised. Optimal resuscitation may be guided by functional measures of fluid responsiveness with some caveats. The best approach may be to use physiologically balanced crystalloids for hypovolemic resuscitation and colloids for euvolemic hemodynamic augmentation. Summary The routine replacement of unmeasured presumed fluid deficits is not appropriate. In critically ill patients, resuscitation with intravenous fluids should produce a demonstrable enhancement of perfusion. Individualized goal-directed therapy using functional hemodynamic parameters can optimize resuscitation and 'deresuscitation'.

Purpose A hospital pharmacy’s efforts to identify and address challenges with bedside scanning of bar codes on large-volume parenteral (LVP) infusion bags are described. Summary Bar-code-assisted medication administration (BCMA) has been shown to reduce medication errors and improve patient safety. After the pilot implementation of a BCMA system and point-of-care scanning procedures at a medical center’s intensive care unit, it was noted that nurses’ attempted bedside scans of certain LVP bags for product identification purposes often were not successful. An investigation and root-cause analysis, including observation of nurses’ scanning technique by a multidisciplinary team, determined that the scanning failures stemmed from the placement of two bar-code imprints—one with the product identification code and another, larger imprint with the expiration date and lot number—adjacently on the LVP bags. The nursing staff was educated on a modified scanning technique, which resulted in significantly improved success rates in the scanning of the most commonly used LVP bags. Representatives of the LVP bag manufacturer met with hospital staff to discuss the problem and corrective measures. As part of a subsequent infusion bag redesign, the manufacturer discontinued the use of the bar-code imprint implicated in the scanning failures. Conclusion Failures in scanning LVP bags were traced to problematic placement of bar-code imprints on the bags. Interdisciplinary collaboration, consultation with the bag manufacturer, and education of the nursing and pharmacy staff resulted in a reduction in scanning failures and the manufacturer’s removal of one of the bar codes from its LVP bags.

Background A recent update of the Mortality Probability Model (MPM)-III found 14% of intensive care patients had age as their only MPM risk factor for hospital mortality. This subgroup had a low mortality rate (2% vs 14% overall), and pronounced differences were noted among elderly patients. This article is an expanded analysis of age-related mortality rates in patients in the ICU. Methods Project IMPACT data from 135 ICUs for 124,885 patients treated from 2001 to 2004 were analyzed. Patients were stratified as elective surgical, emergency/unscheduled surgical, and medical and then further stratified by age and whether additional MPM risk factors were present or absent. Results Mortality rose with advancing age within all patient categories. Elective surgical patients without other risk factors were the least likely to die at all ages (0.4% for patients aged 18-29 years to 9.2% for patients aged ≥ 90 years), whereas medical patients with one or more additional risk factors had the highest mortality rate (12.1% for patients aged 18-29 years to 36.0% for patients aged ≥ 90 years). In these two subsets, mortality rates approximately doubled in the elective surgical group among patients aged in their 70s (2.4%), 80s (4.3%), and 90s (9.2%) but rose less dramatically in the medical group (27.0%, 30.7%, and 36.0%, respectively). Conclusions Although mortality increased with age, the risk differed significantly by patient subset, even among elderly patients, which may reflect a selection bias. Advanced age alone does not preclude successful surgical and ICU interventions, although the presence of serious comorbidities decreases the likelihood of survival to discharge for all age groups.

Scan statistics are concerned with clusters of events over time. In the realm of critical care medicine, such clusters might include the occurrence of ventilator-associated pneumonia (VAP). Given N patients over time, the number of observations in a “moving window” of fixed length can be counted and the maximum cluster value becomes a scan statistic for which both parametric and exact methods exist to calculate its rarity. A statistically unusual cluster may indicate a breakdown in quality. Another approach to monitoring rare events is a g-type statistical process control chart where prospectively observing unusually long periods of time between events can indicate a significant improvement in quality. Both methods are presented in detail and applied to a 24-bed medical/surgical ICU's experience with VAP during a 27-month period.

Patients receiving prolonged acute mechanical ventilation (PAMV), although comprising a third of all mechanical ventilation (MV) patients, consume two-thirds of all the resources allocated to MV, and their numbers are projected to double by 2020. By virtue of their prolonged hospital length of stay (median LOS, 17 days), they are subject to such nosocomial infections as Clostridium difficile-associated disease (CDAD), the incidence and age-adjusted case fatality rate of which doubled between 2000 and 2005. We examined the rates and outcomes of CDAD among adult PAMV patients.We analyzed 2005 data from the Health Care Utilization Project/Nationwide Inpatient Sample from the Agency for Healthcare Research and Quality. PAMV and CDAD were identified using the International Classification of Diseases, ninth revision, clinical modification codes 96.72 and 008.45, respectively.Among 64,910 adult PAMV patients who were discharged in 2005, 3,468 patients (5.34%) had a concurrent diagnosis of CDAD (PAMV patients who were discharged with concomitant diagnosis of CDAD [CDAD+]). CDAD+ patients who were discharged were older (mean [+/- SD] age, 66.7 +/- 15.9 vs 63.7 +/- 16.9 years, respectively; p0.001) and were more likely to have been admitted to the hospital from a long-term care facility (5.7% vs 2.9%, respectively; p0.001) than PAMV patients who were discharged without CDAD (CDAD-). Although crude hospital mortality rates did not differ among PAMV patients who were discharged from the hospital by CDAD status (CDAD+, 32.6%; CDAD-, 33.0%; p = 0.598), both unadjusted calculations and propensity-score adjustment showed a substantial increase in LOS (6.1 days; 95% confidence interval [CI], 4.9 to 7.4) and total costs ($10,355; 95% CI, $7,540 to $13,170) among CDAD+ patients.PAMV patients have an order of magnitude higher risk of having CDAD than other hospitalized patients. Concurrent CDAD infection is associated with increased hospital LOS and costs. The PAMV population is an attractive target for aggressive measures aimed at CDAD prevention.

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 ≥ 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) [ 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.

PURPOSE OF REVIEW The comparison of morbidity, mortality, and length-of-stay outcomes in patients receiving critical care requires adjustment based on their presenting illness. These adjustments are made with severity-of-illness models. These models must be periodically updated to reflect current medical practices. This article will review the history of the Mortality Probability Model (MPM), discuss why and how it was recently updated, and outline examples of MPM use. RECENT FINDINGS All severity-of-illness models have limitations, especially if a unit's patient population becomes highly specialized. In these situations, customized models may provide better accuracy. The MPMs include those calculated at admission (MPM0) and additional models at 24, 48, and 72 h (MPM 24, MPM 48, and MPM 72). The model is now in its third iteration (MPM 0-III). Length of stay (LOS) and subgroup models have also been developed. SUMMARY Understanding appropriate application of models such as MPM is important as transparency in healthcare drives demand for severity-adjusted outcomes data.

Morbidity, mortality, and length-of-stay outcomes in patients receiving critical care are difficult to interpret unless they are risk-stratified for diagnosis, presenting severity of illness, and other patient characteristics. Acuity adjustment systems for adults include the Acute Physiology And Chronic Health Evaluation (APACHE), the Mortality Probability Model (MPM), and the Simplified Acute Physiology Score (SAPS). All have recently been updated and recalibrated to reflect contemporary results. Specialized scores are also available for patient subpopulations where general acuity scores have drawbacks. Demand for outcomes data is likely to grow with pay-for-performance initiatives as well as for routine clinical, prognostic, administrative, and research applications. It is important for clinicians to understand how these scores are derived and how they are properly applied to quantify patient severity of illness and benchmark intensive care unit performance.

To update the Mortality Probability Model at intensive care unit (ICU) admission (MPM0-II) using contemporary data.Retrospective analysis of data from 124,855 patients admitted to 135 ICUs at 98 hospitals participating in Project IMPACT between 2001 and 2004. Independent variables considered were 15 MPM0-II variables, time before ICU admission, and code status. Univariate analysis and multivariate logistic regression were used to identify risk factors associated with hospital mortality.One hundred thirty-five ICUs at 98 hospitals.Patients in the Project IMPACT database eligible for MPM0-II scoring.None.Hospital mortality rate in the current data set was 13.8% vs. 20.8% in the MPM0-II cohort. All MPM0-II variables remained associated with mortality. Clinical conditions with high relative risks in MPM0-II also had high relative risks in MPM0-III. Gastrointestinal bleeding is now associated with lower mortality risk. Two factors have been added to MPM0-III: "full code" resuscitation status at ICU admission, and "zero factor" (absence of all MPM0-II risk factors except age). Seven two-way interactions between MPM0-II variables and age were included and reflect the declining marginal contribution of acute and chronic medical conditions to mortality risk with increasing age. Lead time before ICU admission and pre-ICU location influenced individual outcomes but did not improve model discrimination or calibration. MPM0-III calibrates well by graphic comparison of actual vs. expected mortality, overall standardized mortality ratio (1.018; 95% confidence interval, 0.996-1.040) and a low Hosmer-Lemeshow goodness-of-fit statistic (11.62; p = .31). The area under the receiver operating characteristic curve was 0.823.MPM0-II risk factors remain relevant in predicting ICU outcome, but the 1993 model significantly overpredicts mortality in contemporary practice. With the advantage of a much larger sample size and the addition of new variables and interaction effects, MPM0-III provides more accurate comparisons of actual vs. expected ICU outcomes.

It is 3:00 a.m. on Saturday morning, and there are no empty beds in your ICU. The trauma team has requested a bed for an unstable abdominal gunshot wound victim. Only two current patients are not receiving mechanical ventilation or vasopressors: one is a 63-year-old diabetic, dialysis patient who was admitted 3 days ago for heart failure. After overnight mechanical ventilation and emergency dialysis, she is now extubated and on a highflow oxygen mask. The other is a 75-year-old man who underwent elective coronary bypass surgery on Friday morning and is doing well 12 h after ventilator liberation. Beds are available on wards and a step-down unit. Which patient is at higher risk for readmission or adverse events if discharged to make room for the trauma patient? Intensivists are routinely presented with discharge decisions similar to the one above. In this article we focus on two major concerns when making ICU discharge decisions: the possibility of ICU readmission, and death on a ward or intermediate care unit.

The professional development of early career hospital physicians may be improved by embedding an experienced physician in a coaching role during structured, interdisciplinary team rounds. This article gives a descriptive report of such a model and discusses how it may promote delivery of high-value care to adult inpatients.