A prognostic model for 6-month mortality in elderly survivors of critical illness

Will-to-Live and Self-Rated Health in Older Hospitalized Patients Are Not Predictive for Short-Term Mortality

Background: Self-assessed will-to-live and self-rated health are associated with long-term survival in community-dwelling older persons but have not been examined in frailer older patients in relation to short-term prognosis. The aim was to explore whether will-to-live and self-rated health are predictive for six-month mortality and can guide ceiling of treatment decisions in hospitalized patients in an acute geriatric ward. We included the Surprise Question as reference, being a well-established clinical tool for short-term prognostication. Methods: This multicentric prospective study included patients of 75 years and older admitted at acute geriatric wards of two Belgian hospitals. Will-to-live and self-rated health were scored on a Likert scale (0-5, 0-4) and assessed by junior geriatricians. The senior geriatricians answered the Surprise Question for clinical judgment of prognosis. Receiver-operator characteristic (ROC) curves were constructed to determine diagnostic accuracy. For time-dependent analysis, Cox regression was performed with adjustment for age and gender. Results: Of 93 included patients in the study, 69 were still alive after six months and 24 died, resulting in a six-month mortality of 26%. The mean age was 86 years (range 75-100), 67% of the patients were women. Median will-to-live and self-rated health were 3 (moderate and good). Both will-to-live and self-rated health were not predictive for six-month mortality (area under the ROC curve [AUC] 0.496, p = 0.951 for will-to-live; 0.447, p = 0.442 for self-rated health) as opposed to Surprise Question (AUC 0.793, p < 0.001). After correction for sex and age, the hazard ratio of six-month mortality was 0.92 for will-to-live (p = 0.667), 0.86 for self-rated health (p = 0.548), and 10.28 for Surprise Question (p < 0.001). Conclusion: Will-to-live and self-rated health are not predictive for six-month mortality in patients admitted to the acute geriatric ward, unlike prognostic tools such as Surprise Question. Clinical Trial Registration Number: B670202100792.

A Nomogram for Predicting the Mortality of Patients with Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) is an acute lung injury associated with high morbidity and mortality. This study aimed to establish an accurate prediction model for mortality risk in ARDS. 70% of patients from the Medical Information Mart for Intensive Care Database (MIMIC-III) were selected as the training group, and the remaining 30% as the testing group. Patients from a Chinese hospital were used for external validation. Univariate and multivariate regressions were used to screen the independent predictors. The receiver operating characteristic curve (ROC) analysis, the Hosmer–Lemeshow test, and the calibration curve were used for evaluating the performance of the model. Age, hemoglobin, heart failure, renal failure, Simplified Acute Physiology Score II (SAPS II), immune function impairment, total bilirubin (TBIL), and PaO₂/FiO₂ were identified as independent predictors. The algorithm of the prediction model was: ln (Pr/(1 + Pr)) = −3.147 + 0.037 ∗ age − 0.068 ∗ hemoglobin + 0.522 ∗ heart failure (yes) + 0.487 ∗ renal failure (yes) + 0.029 ∗ SAPS II + 0.697 ∗ immune function impairment (yes) + 0.280 ∗ TBIL (abnormal) − 0.006 ∗ PaO₂/FiO₂ (Pr represents the probability of death occurring). The AUC of the model was 0.791 (0.766–0.816), and the internal and the external validations both confirmed the good performance of the model. A nomogram for predicting the risk of death in ARDS patients was developed and validated. It may help clinicians early identify ARDS patients with high risk of death and thereby help reduce the mortality and improve the survival of ARDS.

Worse pre-admission quality of life is a strong predictor of mortality in critically ill patients

Objectives

In this study, we aimed to investigate whether quality of life (QoL) before intensive care unit (ICU) admission could predict ICU mortality in critically ill patients.

Patients and methods

Between January 2019 and April 2019, a total of 105 ICU patients (54 males, 51 females; mean age: 58 years; range, 18 to 91 years) from two ICUs of a tertiary care hospital were included in this cross-sectional, prospective study. Pre-admission QoL was measured by the Short Form (SF)-12- Physical Component Scores (PCS) and Mental Component Scores (MCS) and EuroQoL five-dimension, five-level scale (EQ-5D-5L) within 24 h of ICU admission and mortality rates were estimated.

Results

The overall mortality rate was 28.5%. Pre-admission QoL was worse in the non-survivors independent from age, sex, socioeconomic and education status, and comorbidities. During the hospitalization, the rate of sepsis and ventilator/hospital-acquired pneumonia were similar among the two groups (p>0.05). Logistic regression analysis adjusted for sex, age, education status, and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores showed that pre-admission functional status as assessed by the SF-12 MCS (odds ratio [OR]: 14,2; 95% confidence interval [CI]: 2.5-79.0), SF-12 PCS (OR: 10.6; 95% CI: 1.8-62.7), and EQ-5D-5L (OR: 8.0; 95% CI: 1.5-44.5) were found to be independently associated with mortality.

Conclusion

Worse pre-admission QoL is a strong predictor of mortality in critically ill patients. The SF-12 and EQ-5D-5L scores are both valuable tools for this assessment. Not only the physical status, but also the mental status before ICU admission should be evaluated in terms of QoL to better utilize ICU resources.

Hospital-specific Template Matching for Benchmarking Performance in a Diverse Multihospital System

BACKGROUND

Hospital-specific template matching is a newer method of hospital performance measurement that may be fairer than regression-based benchmarking. However, it has been tested in only limited research settings.

OBJECTIVE

The objective of this study was to test the feasibility of hospital-specific template matching assessments in the Veterans Affairs (VA) health care system and determine power to detect greater-than-expected 30-day mortality.

RESEARCH DESIGN

Observational cohort study with hospital-specific template matching assessment. For each VA hospital, the 30-day mortality of a representative subset of hospitalizations was compared with the pooled mortality from matched hospitalizations at a set of comparison VA hospitals treating sufficiently similar patients. The simulation was used to determine power to detect greater-than-expected mortality.

SUBJECTS

A total of 556,266 hospitalizations at 122 VA hospitals in 2017.

MEASURES

A number of comparison hospitals identified per hospital; 30-day mortality.

RESULTS

Each hospital had a median of 38 comparison hospitals (interquartile range: 33, 44) identified, and 116 (95.1%) had at least 20 comparison hospitals. In total, 8 hospitals (6.6%) had a significantly lower 30-day mortality than their benchmark, 5 hospitals (4.1%) had a significantly higher 30-day mortality, and the remaining 109 hospitals (89.3%) were similar to their benchmark. Power to detect a standardized mortality ratio of 2.0 ranged from 72.5% to 79.4% for a hospital with the fewest (6) versus most (64) comparison hospitals.

CONCLUSIONS

Hospital-specific template matching may be feasible for assessing hospital performance in the diverse VA health care system, but further refinements are needed to optimize the approach before operational use. Our findings are likely applicable to other large and diverse multihospital systems.

Differences in Characteristics, Hospital Care and Outcomes between Acute Critically Ill Emergency Department Patients with Early and Late Do-Not-Resuscitate Orders

Background: A do-not-resuscitate (DNR) order is associated with an increased risk of death among emergency department (ED) patients. Little is known about patient characteristics, hospital care, and outcomes associated with the timing of the DNR order. Aim: Determine patient characteristics, hospital care, survival, and resource utilization between patients with early DNR (EDNR: signed within 24 h of ED presentation) and late DNR orders. Design: Retrospective observational study. Setting/Participants: We enrolled consecutive, acute, critically ill patients admitted to the emergency intensive care unit (EICU) at Taipei Veterans General Hospital from 1 February 2018, to 31 January 2020. Results: Of the 1064 patients admitted to the EICU, 619 (58.2%) had EDNR and 445 (41.8%) LDNR. EDNR predictors were age >85 years (adjusted odd ratios (AOR) 1.700, 1.027–2.814), living in long-term care facilities (AOR 1.880, 1.066–3.319), having advanced cardiovascular diseases (AOR 2.128, 1.039–4.358), “medical staff would not be surprised if the patient died within 12 months” (AOR 1.725, 1.193–2.496), and patients’ family requesting palliative care (AOR 2.420, 1.187–4.935). EDNR patients underwent lesser endotracheal tube (ET) intubation (15.6% vs. 39.9%, p < 0.001) and had reduced epinephrine injection (19.9% vs. 30.3%, p = 0.009), ventilator support (16.7% vs. 37.9%, p < 0.001), and narcotic use (51.1% vs. 62.6%, p = 0.012). EDNR patients had significantly lower 7-day (p < 0.001), 30-day (p < 0.001), and 90-day (p = 0.023) survival. Conclusions: EDNR patients underwent decreased ET intubation and had reduced epinephrine injection, ventilator support, and narcotic use during EOL as well as decreased length of hospital stay, hospital expenditure, and survival compared to LDNR patients.

Global variations in the prevalence, treatment, and impact of atrial fibrillation in a multi-national cohort of 153 152 middle-aged individuals

AIMS

To compare the prevalence of electrocardiogram (ECG)-documented atrial fibrillation (or flutter) (AF) across eight regions of the world, and to examine antithrombotic use and clinical outcomes.

METHODS AND RESULTS

Baseline ECGs were collected in 153 152 middle-aged participants (ages 35-70 years) to document AF in two community-based studies, spanning 20 countries. Medication use and clinical outcome data (mean follow-up of 7.4 years) were available in one cohort. Cross-sectional analyses were performed to document the prevalence of AF and medication use, and associations between AF and clinical events were examined prospectively. Mean age of participants was 52.1 years, and 57.7% were female. Age and sex-standardized prevalence of AF varied 12-fold between regions; with the highest in North America, Europe, China, and Southeast Asia (270-360 cases per 100 000 persons); and lowest in the Middle East, Africa, and South Asia (30-60 cases per 100 000 persons) (P < 0.001). Compared with low-income countries (LICs), AF prevalence was 7-fold higher in middle-income countries (MICs) and 11-fold higher in high-income countries (HICs) (P < 0.001). Differences in AF prevalence remained significant after adjusting for traditional AF risk factors. In LICs/MICs, 24% of participants with AF and a CHADS2 score ≥1 received antithrombotic therapy, compared with 85% in HICs. AF was associated with an increased risk of stroke [hazard ratio (HR) 2.29; 95% confidence interval (CI) 1.49-3.52] and death (HR 2.97; 95% CI 2.25-3.93); with similar rates in different countries grouped by income level.

CONCLUSIONS

Large variations in AF prevalence occur in different regions and countries grouped by income level, but this is only partially explained by traditional AF risk factors. Antithrombotic therapy is infrequently used in poorer countries despite the high risk of stroke associated with AF.

Elderly Patients in the Intensive Care Unit

Very old intensive care unit (ICU) patients, aged ≥ 80 years, are by no mean newcomers, but during the last decades their impact on ICU admissions has grown in parallel with the increase in the number of elderly persons in the community. Hence, from being a "rarity," they have now become common and constitute one of the largest subgroups within intensive care, and may easily be the largest group in 20 years and make up 30 to 40% of all ICU admissions. Obviously, they are not admitted because they are old but because they are with various diseases and problems like any other ICU patient. However, their age and the presence of common geriatric syndromes such as frailty, cognitive decline, reduced activity of daily life, and several comorbid conditions makes this group particularly challenging, with a high mortality rate. In this review, we will highlight aspects of current and future epidemiology and current knowledge on outcomes, and describe the effects of the aforementioned geriatric syndromes. The major challenge for the coming decades will be the question of whom to treat and the quest for better triage criteria not based on age alone. Challenges with the level of care during the ICU stay will also be discussed. A stronger relationship with geriatricians should be promoted to create a better and more holistic care and aftercare for survivors.

Association of Do-Not-Resuscitate Patient Case Mix With Publicly Reported Risk-Standardized Hospital Mortality and Readmission Rates

IMPORTANCE

The Centers for Medicare and Medicaid Services's (CMS's) 30-day risk-standardized mortality rate (RSMR) and risk-standardized readmission rate (RSRR) models do not adjust for do-not-resuscitate (DNR) status of hospitalized patients and may bias Hospital Readmissions Reduction Program (HRRP) financial penalties and Overall Hospital Quality Star Ratings.

OBJECTIVE

To identify the association between hospital-level DNR prevalence and condition-specific 30-day RSMR and RSRR and the implications of this association for HRRP financial penalty.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study obtained patient-level data from the Medicare Limited Data Set Inpatient Standard Analytical File and hospital-level data from the CMS Hospital Compare website for all consecutive Medicare inpatient encounters from July 1, 2015, to June 30, 2018, in 4484 US hospitals. Hospitalized patients had a principal diagnosis of acute myocardial infarction (AMI), heart failure (HF), stroke, pneumonia, or chronic obstructive pulmonary disease (COPD). Incoming acute care transfers, discharges against medical advice, and patients coming from or discharged to hospice were among those excluded from the analysis.

EXPOSURES

Present-on-admission (POA) DNR status was defined as an International Classification of Diseases, Ninth Revision diagnosis code of V49.86 (before October 1, 2015) or as an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis code of Z66 (beginning October 1, 2015). Hospital-level prevalence of POA DNR status was calculated for each of the 5 conditions.

MAIN OUTCOMES AND MEASURES

Hospital-level 30-day RSMRs and RSRRs for 5 condition-specific cohorts (mortality cohorts: AMI, HF, stroke, pneumonia, and COPD; readmission cohorts: AMI, HF, pneumonia, and COPD) and HRRP financial penalty status (yes or no).

RESULTS

Included in the study were 4 884 237 inpatient encounters across condition-specific 30-day mortality cohorts (patient mean [SD] age, 78.8 [8.5] years; 2 608 182 women [53.4%]) and 4 450 378 inpatient encounters across condition-specific 30-day readmission cohorts (patient mean [SD] age, 78.6 [8.5] years; 2 349 799 women [52.8%]). Hospital-level median (interquartile range [IQR]) prevalence of POA DNR status in the mortality cohorts varied: 11% (7%-16%) for AMI, 13% (7%-23%) for HF, 14% (9%-22%) for stroke, 17% (9%-26%) for pneumonia, and 10% (5%-18%) for COPD. For the readmission cohorts, the hospital-level median (IQR) POA DNR prevalence was 9% (6%-15%) for AMI, 12% (6%-22%) for HF, 16% (8%-24%) for pneumonia, and 9% (4%-17%) for COPD. The 30-day RSMRs were significantly higher for hospitals in the highest quintiles vs the lowest quintiles of DNR prevalence (eg, AMI: 12.9 [95% CI, 12.8-13.1] vs 12.5 [95% CI, 12.4-12.7]; P < .001). The inverse was true among the readmission cohorts, with the highest quintiles of DNR prevalence exhibiting the lowest RSRRs (eg, AMI: 15.3 [95% CI, 15.1-15.5] vs 15.9 [95% CI, 15.7-16.0]; P < .001). A 1% absolute increase in risk-adjusted hospital-level DNR prevalence was associated with greater odds of avoiding HRRP financial penalty (odds ratio, 1.06; 95% CI, 1.04-1.08; P < .001).

CONCLUSIONS AND RELEVANCE

This cross-sectional study found that the lack of adjustment in CMS 30-day RSMR and RSRR models for POA DNR status of hospitalized patients may be associated with biased readmission penalization and hospital-level performance.

Prehospital Frailty and Screening Criteria for Palliative Care Services in Critically Ill Older Adults: An Observational Cohort Study

Background: The use of formalized criteria (or triggers) for palliative care services (PCSs) has been associated with increased use of PCSs in the intensive care unit (ICU). Objective: To explore the utility/validity of frailty as a trigger for providing PCSs. Design: This is a prospective cohort study. Setting/Subjects: Older adults (age ≥50 years) admitted to ICUs were enrolled. Measurements: We measured frailty using the Clinical Frailty Scale. We reviewed electronic health records for the presence/absence of six evidence-based triggers, the use and quality of specialty palliative care (SPC), and markers of primary palliative care (PPC). We used descriptive statistics to describe the differences in PPC, SPC, and six-month mortality by frailty and by the presence/absence of triggers. Results: In a study population of 302 older adults, mean (standard deviation) age 67.2 years (10.5), 151 (50%) were frail and 105 (34.8%) had ≥1 trigger for PCSs. Of the 151 (55.6%) frail patients, 84 had no triggers for PCSs, despite a 46.4% six-month mortality in this group. Patients with ≥1 trigger had higher rates of SPC than those without (39.1% vs. 18.3%, p < 0.001); frail patients also had higher SPC than nonfrail patients (32.5% vs. 18.5%, p = 0.006). Patients with ≥1 trigger had higher rates of PPC than those without (66.7% vs. 44.2%, p < 0.001); no statistically significant difference in PPC was found by frailty (56.3% vs. 47.7%, p = 0.134). Conclusion: The rates of PCSs and six-month mortality by frailty are consistent with frailty being a valid trigger for PCSs in ICUs; the high prevalence of frailty relative to triggers suggests that ways to increase PCSs would be needed.

Validation of the clinical frailty score (CFS) in French language

Background

Very old critical ill patients are a rapid expanding group. To better understand the magnitude of the challenges involved in intensive care practice for an ageing population and discuss a rational allocation of resources, healthcare practitioners need a reliable evaluation of frailty. In order to promote the adequate use of the Clinical Frailty Scale (CFS) in a wider panel of countries, we aimed to develop, validate and characterise a French (FR) version from the original English (EN) CFS.

Methods

We included participants recruited prospectively for the observational “The very old intensive care patient: A multinational prospective observation study” (VIP Study) at Geneva University Hospitals (FR speaking hospital). A FR version of the CFS was obtained by translation (EN- > FR) and back translation (FR- > EN). The final CFS-FR was then evaluated twice on the same participants with at least a 2-week interval by FR-speaking doctors and nurses.

Results

Inter-rater reliability was 0.87 (95%CI: 0.76–0.93) between doctors for the original CFS version and 0.76 (95%CI: 0.57–0.87) between nurses for the FR version. Inter-rater variability between doctor and nurse was 0.75 (95%CI: 0.56–0.87) for the original version, and 0.73 (95%CI: 0.52–0.85) for the FR version.

Test-retest (stability) with the original vs the FR version was 0.86 (95%CI: 0.72–0.93) for doctors and 0.87 (95%CI: 0.76–0.93) for nurses.

Differences between the evaluations of the CFS-EN and CSF-FR were not different from 0, with a mean difference of 0.06 (95%CI -0.24, 0.36) for the EN version and − 0.03 (95%CI -0.47, 0.41) for the FR version. Average original version ratings were slightly lower than FR version ratings, though this difference did not reach significance: -0.29 (95%CI -0.54, 0.04).

Conclusion

In this prospective cohort of very old intensive care participants we developed and tested the basic psychometric properties (internal consistency, reproducibility) of a French version of the CFS. This manuscript provides clinically meaningful psychometric properties that have not been previously reported in any other language, including in the original EN version.

The French cultural adaptation of this CFS has adequate psychometric properties for doctors or nurses to evaluate frailty in very old intensive care patients.

Factors Affecting Discharge to Home of Medical Patients Treated in an Intensive Care Unit

The purpose of this study was to examine the factors affecting the discharge to home of medical patients treated in an intensive care unit, including elements of in-hospital rehabilitation and prehospital movement ability. The participants of this retrospective cohort study were medical patients treated in an intensive care unit (ICU) and who began rehabilitation in ICU. We assessed the participants in the ICU and analyzed data on patient background, hospitalization, and rehabilitation status. There were 155 ICU patients available for analysis. A multivariable logistic regression model identified the four variables of age (OR 1.06, 95% CI 1.02–1.09), APACHE II score (OR 1.12, 95% CI 1.04–1.24), independence in home life before admission (OR 7.10, 95% CI 1.65–30.44), and standing within 5 days of admission (OR 6.58, 95% CI 2.60–16.61) as factors significantly related to discharge from hospital to home. Independence of home life before admission and early start of standing were identified as factors strongly related to discharge to home. The degree of independence in living before hospital admission and progress toward early mobilization are helpful when considering an ICU patient’s discharge destination.

Supporting the Geriatric Critical Care Patient: Decision Making, Understanding Outcomes, and the Role of Palliative Care

Geriatric admissions to the intensive care unit (ICU) are common and require unique considerations for ICU clinicians. Admission to the ICU should be considered on an individual-patient basis. It is reasonable to consider a "trial of critical care" for many patients, even those who have uncertain chances of meaningful recovery. Quality of life and functional independence are especially important to older adults, and these outcomes should be considered when weighing the risks and benefits of admission or continuing ICU care.

Health Insurance and Disparities in Mortality among Older Survivors of Critical Illness: A Population Study

OBJECTIVES

The 1.5 million Medicare beneficiaries who survive intensive care each year have a high post-hospitalization mortality rate. We aimed to determine whether mortality after critical illness is higher for Medicare beneficiaries with Medicaid compared with those with commercial insurance.

DESIGN

A retrospective cohort study from 2010 through 2014 with 1 year of follow-up using the New York Statewide Planning and Research Cooperative System database.

SETTING

A New York State population-based study of older (age ≥65 y) survivors of intensive care.

PARTICIPANTS

Adult Medicare beneficiaries age 65 years or older who were hospitalized with intensive care at a New York State hospital and survived to discharge.

INTERVENTION

None.

MEASUREMENT

Mortality in the first year after hospital discharge.

RESULTS

The study included 340 969 Medicare beneficiary survivors of intensive care with a mean (standard deviation) age of 77 (8) years; 20% died within 1 year. There were 152 869 (45%) with commercial insurance, 78 577 (23%) with Medicaid, and 109 523 (32%) with Medicare alone. Compared with those with commercial insurance, those with Medicare alone had a similar 1-year mortality rate (adjusted hazard ratio [aHR] = 1.01; 95% confidence interval [CI] = .99-1.04), and those with Medicaid had a 9% higher 1-year mortality rate (aHR = 1.09; 95% CI = 1.05-1.12). Among those discharged home, the 1-year mortality rate did not vary by insurance coverage, but among those discharged to skilled-care facilities (SCFs), the 1-year mortality rate was 16% higher for Medicaid recipients (aHR = 1.16; 95% CI = 1.12-1.21; P for interaction <.001).

CONCLUSIONS

Older adults with Medicaid insurance have a higher 1-year post-hospitalization mortality compared with those with commercial insurance, especially among those discharged to SCFs. Future studies should investigate care disparities at SCFs that may mediate these higher mortality rates. J Am Geriatr Soc 67:2497-2504, 2019.

Predicting need for advanced illness or palliative care in a primary care population using electronic health record data

Timely outreach to individuals in an advanced stage of illness offers opportunities to exercise decision control over health care. Predictive models built using Electronic health record (EHR) data are being explored as a way to anticipate such need with enough lead time for patient engagement. Prior studies have focused on hospitalized patients, who typically have more data available for predicting care needs. It is unclear if prediction driven outreach is feasible in the primary care setting. In this study, we apply predictive modeling to the primary care population of a large, regional health system and systematically examine the impact of technical choices, such as requiring a minimum number of health care encounters (data density requirements) and aggregating diagnosis codes using Clinical Classifications Software (CCS) groupings to reduce dimensionality, on model performance in terms of discrimination and positive predictive value. We assembled a cohort of 349,667 primary care patients between 65 and 90 years of age who sought care from Sutter Health between July 1, 2011 and June 30, 2014, of whom 2.1% died during the study period. EHR data comprising demographics, encounters, orders, and diagnoses for each patient from a 12 month observation window prior to the point when a prediction is made were extracted. L1 regularized logistic regression and gradient boosted tree models were fit to training data and tuned by cross validation. Model performance in predicting one year mortality was assessed using held-out test patients. Our experiments systematically varied three factors: model type, diagnosis coding, and data density requirements. We found substantial, consistent benefit from using gradient boosting vs logistic regression (mean AUROC over all other technical choices of 84.8% vs 80.7% respectively). There was no benefit from aggregation of ICD codes into CCS code groups (mean AUROC over all other technical choices of 82.9% vs 82.6% respectively). Likewise increasing data density requirements did not affect discrimination (mean AUROC over other technical choices ranged from 82.5% to 83%). We also examine model performance as a function of lead time, which is the interval between death and when a prediction was made. In subgroup analysis by lead time, mean AUROC over all other choices ranged from 87.9% for patients who died within 0 to 3 months to 83.6% for those who died 9 to 12 months after prediction time.

Outcome of older persons admitted to intensive care unit, mortality, prognosis factors, dependency scores and ability trajectory within 1 year: a prospective cohort study

BACKGROUND

The outcome and functional trajectory of older persons admitted to intensive care (ICU) unit remain a true question for critical care physicians and geriatricians, due to the heterogeneity of geriatric population, heterogeneity of practices and absence of guidelines.

AIM

To describe the 1-year outcome, prognosis factors and functional trajectory for older people admitted to ICU.

METHODS

In a prospective 1-year cohort study, all patients aged 75 years and over admitted to our ICU were included according to a global comprehensive geriatric assessment. Follow-up was conducted for 1 year survivors, in particular, ability scores and living conditions.

RESULTS

Of 188 patients included [aged 82.3 ± 4.7 years, 46% of admissions, median SAPS II 53.5 (43-74), ADL of Katz's score 4.2 ± 1.6, median Barthel's index 71 (55-90), AGGIR scale 4.5 ± 1.5], the ICU, hospital and 1-year mortality were, respectively, 34, 42.5 and 65.5%. Prognosis factors were: SAPS 2, mechanical ventilation, comorbidity (Lee's and Mc Cabe's scores), disability scores (ADL of Katz's score, Barthel's index and AGGIR scale), admission creatinin, hypoalbuminemia, malignant haemopathy, cognitive impairment. One-year survivors lived in their own home for 83%, with a preserved physical ability, without significant variation of the three ability assessed scores compared to prior ICU admission.

CONCLUSION

The mortality of older people admitted to ICU is high, with a significant impact of disabilty scores, and preserved 1-year survivor independency. Other studies, including a better comprehensive geriatric assessment, seem necessary to determine a predictive "phenotype" of survival with a "satisfactory" level of autonomy.

Frailty and the Association Between Long-Term Recovery After Intensive Care Unit Admission

Frailty is common, although infrequently screened for among patients admitted to intensive care. Frailty has been the focus of research in geriatric medicine; however, its epidemiology and interaction with critical illness have only recently been studied. Instruments to screen for and measure frailty require refinement in intensive care settings. Frail critically ill patients are at higher risk of poor outcomes. Frail survivors of critical illness are high users of health resources. Further research is needed to understand how frailty assessment can inform decision-making before and during an episode of critical illness and during an intensive care course for frail patients.

Derivation and Validation of a Prognostic Model to Predict 6-Month Mortality in an Intensive Care Unit Population

RATIONALE

Identification of terminally ill patients in the intensive care unit (ICU) would facilitate decision making and timely palliative care.

OBJECTIVES

To develop and validate a patient-specific integrated prognostic model to predict 6-month mortality in medical ICU patients.

METHODS

A longitudinal prospective cohort study of temporally split samples of 1,049 consecutive medical ICU patients in a tertiary care hospital was performed. For each patient, we collected demographic data, Acute Physiology and Chronic Health Evaluation III score, Charlson comorbidity index, intensivist response to a surprise question (SQ; "Would I be surprised if this patient died in the next 6 months?") on admission, and vital status at 6 months.

RESULTS

Between November 2013 and May 2015, derivation and validation cohorts of 500 and 549 consecutive patients were studied to develop a multivariate logistic regression model. In the multivariate logistic regression model, Charlson comorbidity index (P = 0.033), Acute Physiology and Chronic Health Evaluation III score (P < 0.001), and SQ response (P < 0.001) were predictors of vital status at 6 months. The odds of dying within 6 months were significantly higher when the SQ was answered "no" than when it was answered "yes" (odds ratio, 7.29; P < 0.001). The c-statistic for the derivation and validation cohorts were 0.832 (95% confidence interval, 0.795-0.870) and 0.84 (95% confidence interval, 0.806-0.875), respectively.

CONCLUSIONS

Our integrated prognostic model, which includes the SQ, has strong discrimination and calibration to predict 6-month mortality in medical ICU patients. This model can aid clinicians in identifying ICU patients who may benefit from the integration of palliative care into their treatment.

Outcomes and Mortality Prediction Model of Critically Ill Adults With Acute Respiratory Failure and Interstitial Lung Disease

BACKGROUND

We aimed to examine short- and long-term mortality in a mixed population of patients with interstitial lung disease (ILD) with acute respiratory failure, and to identify those at lower vs higher risk of in-hospital death.

METHODS

We conducted a single-center retrospective cohort study of 126 consecutive adults with ILD admitted to an ICU for respiratory failure at a tertiary care hospital between 2010 and 2014 and who did not undergo lung transplantation during their hospitalization. We examined associations of ICU-day 1 characteristics with in-hospital and 1-year mortality, using Poisson regression, and examined survival using Kaplan-Meier curves. We created a risk score for in-hospital mortality, using a model developed with penalized regression.

RESULTS

In-hospital mortality was 66%, and 1-year mortality was 80%. Those with connective tissue disease-related ILD had better short-term and long-term mortality compared with unclassifiable ILD (adjusted relative risk, 0.6; 95% CI, 0.3-0.9; and relative risk, 0.6; 95% CI, 0.4-0.9, respectively). Our prediction model includes male sex, interstitial pulmonary fibrosis diagnosis, use of invasive mechanical ventilation and/or extracorporeal life support, no ambulation within 24 h of ICU admission, BMI, and Simplified Acute Physiology Score-II. The optimism-corrected C-statistic was 0.73, and model calibration was excellent (P = .99). In-hospital mortality rates for the low-, moderate-, and high-risk groups were 33%, 65%, and 96%, respectively.

CONCLUSIONS

We created a risk score that classifies patients with ILD with acute respiratory failure from low to high risk for in-hospital mortality. The score could aid providers in counseling these patients and their families.

How does prior health status (age, comorbidities and frailty) determine critical illness and outcome?

PURPOSE OF REVIEW

Critical illness has a significant impact on an individual's physical and mental health. However, it is less clear to what degree outcomes after critical illness are due to patients' preexisting characteristics, rather than the critical illness itself. In this review, we summarize recent findings regarding the role of age, comorbidity and frailty on long-term outcomes after critical illness.

RECENT FINDINGS

Age, comorbidity and frailty are all associated with an increased risk of critical illness. Although severity of illness drives the risk of acute mortality, recent data suggest that longer term outcomes are much more closely aligned with prior health status. There are growing data regarding the important role of noncardiovascular comorbidity, including psychiatric illness and obesity, in determining long-term outcomes. Finally, preadmission frailty is associated with poor long-term outcomes after critical illness; further data are needed to evaluate the attributable impact of critical illness on the health trajectories of frail individuals.

SUMMARY

Age, comorbidity and frailty play a critical role in determining the long-term outcomes of patients requiring intensive care.

Association between Do Not Resuscitate/Do Not Intubate Status and Resident Physician Decision-making. A National Survey

RATIONALE

Compared with their Full Code counterparts, patients with do not resuscitate/do not intubate (DNR/DNI) status receive fewer interventions and have higher mortality than predicted by clinical characteristics.

OBJECTIVES

To assess whether internal medicine residents, the front-line providers for many hospitalized patients, would manage hypothetical patients differently based on code status. We hypothesized respondents would be less likely to provide a variety of interventions to DNR/DNI patients than to Full Code patients.

METHODS

Cross-sectional, randomized survey of U.S. internal medicine residents. We created two versions of an internet survey, each containing four clinical vignettes followed by questions regarding possible interventions; the versions were identical except for varying code status of the vignettes. Residency programs were randomly allocated between the two versions.

RESULTS

Five hundred thirty-three residents responded to the survey. As determined by Chi-squared and Fisher's exact test, decisions to intubate or perform cardiopulmonary resuscitation were largely dictated by patient code status (>94% if Full Code, <5% if DNR/DNI; P < 0.0001 for all scenarios). Resident proclivity to deliver noninvasive interventions (e.g., blood cultures, medications, imaging) was uniformly high (>90%) and unaffected by code status. However, decisions to pursue other aggressive or invasive options (e.g., dialysis, bronchoscopy, surgical consultation, transfer to intensive care unit) differed significantly based on code status in most vignettes.

CONCLUSIONS

Residents appear to assume that patients who would refuse cardiopulmonary resuscitation would prefer not to receive other interventions. Without explicit clarification of the patient's goals of care, potentially beneficial care may be withheld against the patient's wishes.

Accounting for Patient Preferences Regarding Life-Sustaining Treatment in Evaluations of Medical Effectiveness and Quality

The importance of understanding patient preferences for life-sustaining treatment is well described for individual clinical decisions; however, its role in evaluations of healthcare outcomes and quality has received little attention. Decisions to limit life-sustaining therapies are strongly associated with high risks for death in ways that are unaccounted for by routine measures of illness severity. However, this essential information is generally unavailable to researchers, with the potential for spurious inferences. This may lead to "confounding by unmeasured patient preferences" (a type of confounding by indication) and has implications for assessments of treatment effectiveness and healthcare quality, especially in acute and critical care settings in which risk for death and adverse events are high. Through a collection of case studies, we explore the effect of unmeasured patient resuscitation preferences on issues critical for researchers and research consumers to understand. We then propose strategies to more consistently elicit, record, and harmonize documentation of patient preferences that can be used to attenuate confounding by unmeasured patient preferences and provide novel opportunities to improve the patient centeredness of medical care for serious illness.

Variation in Do-Not-Resuscitate Orders and Implications for Heart Failure Risk-Adjusted Hospital Mortality Metrics

OBJECTIVES

This study evaluated the effect of patient do-not-resuscitate (DNR) status on hospital risk-adjusted heart failure mortality metrics.

BACKGROUND

Do-not-resuscitate orders limit the use of life-sustaining therapies. Patients with DNR orders have increased in-hospital mortality, and DNR rates vary among hospitals. Variations in DNR rates could strongly confound risk-adjusted hospital mortality rates for heart failure.

METHODS

We identified a cohort of adults with primary diagnosis of heart failure by using the 2011 California State Inpatient Database, a claims database that captures "early DNR," within 24 h of admission. Hospital-level risk-standardized in-hospital mortality was determined using random effects logistic regression. We explored changes in outlier status in models with and without early DNR status.

RESULTS

Among 55,865 patients from 290 hospitals hospitalized with heart failure, 12.1% (11.8% to 12.4%) had an early DNR order. Hospitals with higher risk-standardized DNR rates had higher risk-standardized mortality (ρ = 0.241; 95% confidence interval [CI]: 0.129 to 0.346; p < 0.001). Including DNR in models used to benchmark hospital mortality improved model performance (c-statistic from 0.821 [95% CI: 0.812 to 0.830] to 0.845 [95% CI: 0.837 to 0.853]; increased model explanatory power by 17%). Including DNR resulted in reclassification of 9.3% of hospitals' outlier status. Agreement in hospital outlier designation between models with and without DNR was low to moderate (kappa coefficient: 0.492; 95% CI: 0.331 to 0.654).

CONCLUSIONS

Accounting for DNR status resulted in a change in estimated risk-standardized mortality rates and classification of hospitals as performance "outliers." Given public reporting of heart failure mortality measurements and their influence on reimbursement, accounting for the presence of early DNR orders in quality measures should be considered.

Race, Ethnicity, Health Insurance, and Mortality in Older Survivors of Critical Illness

OBJECTIVES

To determine whether minority race or ethnicity is associated with mortality and mediated by health insurance coverage among older (≥ 65 yr old) survivors of critical illness.

DESIGN

A retrospective cohort study.

SETTING

Two New York City academic medical centers.

PATIENTS

A total of 1,947 consecutive white (1,107), black (361), and Hispanic (479) older adults who had their first medical-ICU admission from 2006 through 2009 and survived to hospital discharge.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We obtained demographic, insurance, and clinical data from electronic health records, determined each patient's neighborhood-level socioeconomic data from 2010 U.S. Census tract data, and determined death dates using the Social Security Death Index. Subjects had a mean (SD) age of 79 years (8.6 yr) and median (interquartile range) follow-up time of 1.6 years (0.4-3.0 yr). Blacks and Hispanics had similar mortality rates compared with whites (adjusted hazard ratio, 0.92; 95% CI, 0.76-1.11 and adjusted hazard ratio, 0.92; 95% CI, 0.76-1.12, respectively). Compared to those with commercial insurance and Medicare, higher mortality rates were observed for those with Medicare only (adjusted hazard ratio, 1.43; 95% CI, 1.03-1.98) and Medicaid (adjusted hazard ratio, 1.30; 95% CI, 1.10-1.52). Medicaid recipients who were the oldest ICU survivors (> 82 yr), survivors of mechanical ventilation, and discharged to skilled-care facilities had the highest mortality rates (p-for-interaction: 0.08, 0.03, and 0.17, respectively).

CONCLUSIONS

Mortality after critical illness among older adults varies by insurance coverage but not by race or ethnicity. Those with federal or state insurance coverage only had higher mortality rates than those with additional commercial insurance.

Survival and medical utilization of children and adolescents with prolonged ventilator-dependent and associated factors

Over the course of a year, more than 20,000 patients in Taiwan require prolonged mechanical ventilation (PMV). Data from the National Health Insurance Research Database for patients between 2005 and 2011 were used to conduct a retrospective analysis on ventilator dependence. The study subjects were PMV patients aged <17 years in Taiwan. A multiple regression model employing general estimating equations was applied to investigate the factors affecting the use of medical resources by children and adolescent PMV patients. A Cox proportional hazard model was incorporated to explore the factors affecting the survival of these patients. Data were collected for a total of 1,019 children and adolescent PMV patients in Taiwan. The results revealed that the average number of outpatient visits per subject was 32.1 times per year, whereas emergency treatments averaged 1.56 times per year per subject and hospitalizations averaged 160.8 days per year per subject. Regarding average annual medical costs, hospitalizations accounted for the largest portion at NT$821,703 per year per subject, followed by outpatient care at NT$123,136 per year per subject and emergency care at NT$3,806 per year per subject. The demographic results indicated that the patients were predominately male (61.24%), with those under 1 year of age accounting for the highest percentage (36.38%). According to the Kaplan—Meier curve, the 1-year and 5-year mortality rates of the patients were approximately 32% and 47%, respectively. The following factors affecting the survival rate were considered: age, the Charlson Comorbidity Index (CCI), diagnosis type necessitating ventilator use, and whether an invasive ventilator was used. This study investigated the use of medical resources and the survival rates of children and adolescent PMV patients. The findings of this study can serve as a reference for the National Health Insurance Administration in promoting its future integrated pilot projects on ventilator dependency.

Pre-admission functional status impacts the performance of the APACHE IV model of mortality prediction in critically ill patients

BACKGROUND

Functional status (FS) before intensive care unit (ICU) admission is associated with short-term and long-term outcomes among critically ill patients. However, measures of FS are generally not integrated into ICU-specific mortality prediction models.

METHODS

This retrospective cohort study used prospectively collected data from 9638 consecutive patients admitted to a single ICU between 1 October 2005 and 30 September 2015. For each ICU admission, FS was prospectively determined and classified into three discrete categories based on performance of basic daily living activities (FS1 - fully independent; FS2 - partly dependent; FS3 - completely dependent). We prospectively calculated Acute Physiology and Chronic Health Evaluation (APACHE) IV predicted mortality percentage (APIV PM) for each admission and calculated observed-expected mortality ratios (OEMR), stratified by FS category and APIV PM. We calculated area under the receiver operator characteristic curve (AUC) for APIV PM and mortality for the entire cohort and the three FS categories.

RESULTS

Patients had a median (IQR) age of 67 (52-80) years and mean (SD) APIV PM was 18.3% (24.3%). Of these, 7714 (80.0%) were classified as FS1, 1728 (17.9%) as FS2 and 196 (2.0%) as FS3. FS1 patients were younger, had less comorbid disease, and lower APIV PM compared to FS2 and FS3. The OEMR were significantly lower for FS1 (0.67) than FS2 (0.93) or FS3 (0.90) (p < 0.0001 for both comparisons). Among patients with APIV PM 0-10%, 10-25%, 25-50% and ≥50% the OEMR for FS1 were 0.33, 0.49, 0.61 and 0.86. The AUC (95% CI) for APIV PM and mortality for FS1, FS2 and FS3 were 0.924 (0.914-0.933), 0.837 (0.816-0.858) and 0.775 (0.705-0.8456), respectively (p < 0.001 for each comparison). Multivariable analysis demonstrated that FS2 (OR 2.18 (1.84-2.57) (p < 0.0001)) and FS3 (OR 1.99 (1.34-2.96) (p = 0.0006)) were independently associated with increased risk of mortality.

CONCLUSIONS

Baseline FS prior to critical illness is a strong independent predictor of mortality and impacts the relationship between observed and APIV PM in those with lower illness severity. Future iterations of mortality prediction models should integrate a baseline measure of FS to improve performance.

Can body mass index predict clinical outcomes for patients with acute lung injury/acute respiratory distress syndrome? A meta-analysis

BACKGROUND

The effects of body mass index (BMI) on the prognosis of acute respiratory distress syndrome (ARDS) are controversial. We aimed to further determine the relationship between BMI and the acute outcomes of patients with ARDS.

METHODS

We searched the Pubmed, Embase, Medline, Cochrane Central Register of Controlled Trials (CENTRAL), and ISI Web of Science for trials published between 1946 and July 2016, using "BMI" or "body mass index" or "overweight" or "obese" and "ARDS" or "ALI" or "acute respiratory distress syndrome" or "acute lung injury", without limitations on publication type or language. Heterogeneity and sensitivity analyses were conducted, and a random-effects model was applied to calculate the odds ratio (OR) or mean difference (MD). Review Manager (RevMan) was used to test the hypothesis using the Mann-Whitney U test. The primary outcome was unadjusted mortality, and secondary outcomes included mechanical ventilation (MV)-free days and length of stay (LOS) in the intensive care unit (ICU) and in hospital.

RESULTS

Five trials with a total of 6268 patients were pooled in our final analysis. There was statistical heterogeneity between normal-weight and overweight patients in LOS in the ICU (I ² = 71%, χ ² = 10.27, P = 0.02) and in MV-free days (I ² = 89%, χ ² = 18.45, P < 0.0001). Compared with normal weight, being underweight was associated with higher mortality (OR 1.59, 95% confidence interval (CI) 1.22, 2.08, P = 0.0006), while obesity and morbid obesity were more likely to result in lower mortality (OR 0.68, 95% CI 0.57, 0.80, P < 0.00001; OR 0.72, 95% CI 0.56, 0.93, P = 0.01). MV-free days were much longer in patients with morbid obesity (MD 2.64, 95% CI 0.60, 4.67, P = 0.01), but ICU and hospital LOS were not influenced by BMI. An important limitation of our analysis is the lack of adjustment for age, sex, illness severity, comorbid illness, and interaction of outcome parameters.

CONCLUSIONS

Obesity and morbid obesity are associated with lower mortality in patients with ARDS.

A Comprehensive Multimorbidity Index for Predicting Mortality in Intensive Care Unit Patients

BACKGROUND

Accurate prediction of mortality for patients admitted to the intensive care units (ICUs) is an important component of medical care. However, little is known about the role of multimorbidity in predicting end of life for high-risk and vulnerable patients.

OBJECTIVE

The aim of the study was to derive and validate a multimorbidity risk model in an attempt to predict all-cause mortality at 6 and 12 months posthospital discharge.

METHODS

This is a retrospective, observational, clinical cohort study. Data were collected on 442,692 ICU patients who received care through the Veterans Administration between January 2003 and December 2013. The primary outcome was all-cause mortality at 6 and 12 months posthospital discharge. We divided the data into derivation (80%) and validation (20%) sets. Using multivariable logistic regression models, we compared prognostic models based on age, principal diagnosis groups, physiological markers, immunosuppressants, comorbidity categories, and a newly developed multimorbidity index (MMI) based on 5695 comorbidities. The cross-validated area under the receiver operating characteristic curve (AUC) was used to report the accuracy of predicting all-cause mortality at 6 and 12 months of hospital discharge.

RESULTS

The average age of patients was 68.87 years (standard deviation = 12.1), 95.9% were males, 44.9% were widowed, divorced, or separated. The relative order of accuracy in predicting mortality was the MMI (AUC = 0.84, CI = 0.83-0.84), VA Inpatient Evaluation Center index (AUC = 0.80, CI = 0.79-0.81), principal diagnosis groups (AUC = 0.74, CI = 0.73-0.74), comorbidities (AUC = 0.69, CI = 0.68-0.70), physiological markers (AUC = 0.65, CI = 0.64-0.65), age (AUC = 0.60, CI = 0.60-0.61),and immunosuppressant use (AUC = 0.59, CI = 0.58-0.59).

CONCLUSIONS

The MMI improved the accuracy of predicting short- and long-term all-cause mortality for ICU patients. Further prospective studies are needed to validate the index in different clinical settings and test generalizability of results in patients outside the VA system of care.

Are Trends in Hospitalization Prior to Hospice Use Associated With Hospice Episode Characteristics?

This study expands current knowledge of factors associated with initiation of hospice care by examining prehospice patterns of medical care leading to Medicare hospice use and the relationships to hospice episode characteristics. Data from the Atherosclerosis Risk in Communities (ARIC) study cohort offer the ability to control for measures that are not available in Medicare claims data, including marital status, nursing home residency, and education. For 1248 ARIC participants who used hospice (2006-2012), participant level trends in the number of hospital days per 30-day period over the year prior to hospice initiation were generated using a fixed-effects model. Logistic regression was used to estimate the associations between increasing hospital use over the year prior to hospice enrollment with key patient characteristics (diagnosis, age, and comorbidity) and episode characteristics (short hospice stay ending in death, long hospice stay, and live discharge). Participants with severe comorbidity (measured as a Charlson comorbidity index score greater than 5) had higher odds of increasing hospital use prior to hospice (odds ratio [OR] = 3.28, confidence interval [CI] = 2.25-4.78). Increasing hospital use did not vary by diagnosis but was associated with reduced odds of a live hospice discharge (OR = 0.55, CI = 0.34-0.88) or long stay in hospice (OR = 0.44, CI = 0.24-0.79) and increased odds of a short stay in hospice (OR = 1.92, CI = 1.36-2.71). The evidence that care patterns prior to hospice use are associated with hospice outcomes could facilitate development of interventions to improve timely hospice referral.

A prospective multicenter cohort study of frailty in younger critically ill patients

Background

Frailty is a multidimensional syndrome characterized by loss of physiologic and cognitive reserve that heightens vulnerability. Frailty has been well described among elderly patients (i.e., 65 years of age or older), but few studies have evaluated frailty in nonelderly patients with critical illness. We aimed to describe the prevalence, correlates, and outcomes associated with frailty among younger critically ill patients.

Methods

We conducted a prospective cohort study of 197 consecutive critically ill patients aged 50–64.9 years admitted to intensive care units (ICUs) at six hospitals across Alberta, Canada. Frailty was defined as a score ≥5 on the Clinical Frailty Scale before hospitalization. Multivariable analyses were used to evaluate factors independently associated with frailty before ICU admission and the independent association between frailty and outcome.

Results

In the 197 patients in the study, mean (SD) age was 58.5 (4.1) years, 37 % were female, 73 % had three or more comorbid illnesses, and 28 % (n = 55; 95 % CI 22–35) were frail. Factors independently associated with frailty included not being completely independent (adjusted OR [aOR] 4.4, 95 % CI 1.8–11.1), connective tissue disease (aOR 6.0, 95 % CI 2.1–17.0), and hospitalization within the preceding year (aOR 3.3, 95 % CI 1.3–8.1). There were no significant differences between frail and nonfrail patients in reason for admission, Acute Physiology and Chronic Health Evaluation II score, preference for life support, or treatment intensity. Younger frail patients did not have significantly longer (median [interquartile range]) hospital stay (26 [9–68] days vs. 19 [10–43] days; p = 0.4), but they had greater 1-year rehospitalization rates (61 % vs. 40 %; p = 0.02) and higher 1-year mortality (33 % vs. 20 %; adjusted HR 1.8, 95 % CI 1.0–3.3; p = 0.039).

Conclusions

Prehospital frailty is common among younger critically ill patients, and in this study it was associated with higher rates of mortality at 1 year and with rehospitalization. Our data suggest that frailty should be considered in younger adults admitted to the ICU, not just in the elderly. Additional research is needed to further characterize frailty in younger critically ill patients, along with the ideal instruments for identification.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-016-1338-x) contains supplementary material, which is available to authorized users.

Severe sepsis in older adults

Severe sepsis may be underrecognized in older adults. Therefore, the purpose of this article is to review special considerations related to early detection of severe sepsis in older adults. Normal organ changes attributed to aging may delay early detection of sepsis at the time when interventions have the greatest potential to improve patient outcomes. Systems are reviewed for changes. For example, the cardiovascular system may have a limited or absent compensatory response to inflammation after an infectious insult, and the febrile response and recruitment of white blood cells may be blunted because of immunosenescence in aging. Three of the 4 hallmark responses (temperature, heart rate, and white blood cell count) to systemic inflammation may be diminished in older adults as compared with younger adults. It is important to consider that older adults may not always manifest the typical systemic inflammatory response syndrome. Atypical signs such as confusion, decreased appetite, and unsteady gait may occur before sepsis related organ failure. Systemic inflammatory response syndrome criteria and a comparison of organ failure criteria were reviewed. Mortality rates in sepsis and severe sepsis remain high and are often complicated by multiple organ failures. As the numbers of older adults increase, early identification and prompt treatment is crucial in improving patient outcomes.

Association of Do-Not-Resuscitate Orders and Hospital Mortality Rate Among Patients With Pneumonia

IMPORTANCE

Hospital quality measures that do not account for patient do-not-resuscitate (DNR) status may penalize hospitals admitting a greater proportion of patients with limits on life-sustaining treatments.

OBJECTIVE

To evaluate the effect of analytic approaches accounting for DNR status on risk-adjusted hospital mortality rates and performance rankings.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective, population-based cohort study was conducted among adults hospitalized with pneumonia in 303 California hospitals between January 1 and December 31, 2011. We used hierarchical logistic regression to determine associations between patient DNR status, hospital-level DNR rates, and mortality measures. Changes in hospital risk-adjusted mortality rates after accounting for patient DNR status and interhospital variation in the association between DNR status and mortality were examined. Data analysis was conducted from January 16 to September 16, 2015.

EXPOSURES

Early DNR status (within 24 hours of admission).

MAIN OUTCOMES AND MEASURES

In-hospital mortality, determined using hierarchical logistic regression.

RESULTS

A total of 90,644 pneumonia cases (5.4% of admissions) were identified among the 303 California hospitals evaluated during 2011; mean (SD) age of the patients was 72.5 (13.7) years, 51.5% were women, and 59.3% were white. Hospital DNR rates varied (median, 15.8%; 25th-75th percentile, 8.9%-22.3%). Without accounting for patient DNR status, higher hospital-level DNR rates were associated with increased patient mortality (adjusted odds ratio [OR] for highest-quartile DNR rate vs lowest quartile, 1.17; 95% CI, 1.04-1.32), corresponding to worse hospital mortality rankings. In contrast, after accounting for patient DNR status and between-hospital variation in the association between DNR status and mortality, hospitals with higher DNR rates had lower mortality (adjusted OR for highest-quartile DNR rate vs lowest quartile, 0.79; 95% CI, 0.70-0.89), with reversal of associations between hospital mortality rankings and DNR rates. Only 14 of 27 hospitals (51.9%) characterized as low-performing outliers without accounting for DNR status remained outliers after DNR adjustment. Hospital DNR rates were not significantly associated with composite quality measures of processes of care for pneumonia (r = 0.11; P = .052); however, DNR rates were positively correlated with patient satisfaction scores (r = 0.35; P < .001).

CONCLUSIONS AND RELEVANCE

Failure to account for DNR status may confound the evaluation of hospital quality using mortality outcomes, penalizing hospitals that admit a greater proportion of patients with limits on life-sustaining treatments. Stakeholders should seek to improve methods to standardize and report DNR status in hospital discharge records to allow further assessment of implications of adjusting for DNR in quality measures.

Clinical conundrums in management of sepsis in the elderly

In 2012, surviving sepsis campaign came out with updated international guidelines for management of severe sepsis and septic shock. Paradoxically, there are no specific guidelines for management of sepsis in the elderly, although the elderly are more predisposed to sepsis, and morbidity and mortality related to sepsis. Sepsis in the elderly is, more often than not, complicated by clinical conundrums such as congestive heart failure (CHF), atrial fibrillation (AF), chronic kidney disease (CKD), acute kidney injury (AKI), delirium, dementia, ambulatory dysfunction, polypharmacy, malglycemia, nutritional deficiencies, and antibiotic resistance. Also, with recurrent admissions to the hospital and widespread use of antibiotics, the elderly are more susceptible to Clostridium difficile colitis.

Long-term association between frailty and health-related quality of life among survivors of critical illness: a prospective multicenter cohort study

OBJECTIVE

Frailty is a multidimensional syndrome characterized by loss of physiologic reserve that gives rise to vulnerability to poor outcomes. We aimed to examine the association between frailty and long-term health-related quality of life among survivors of critical illness.

DESIGN

Prospective multicenter observational cohort study.

SETTING

ICUs in six hospitals from across Alberta, Canada.

PATIENTS

Four hundred twenty-one critically ill patients who were 50 years or older.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Frailty was operationalized by a score of more than 4 on the Clinical Frailty Scale. Health-related quality of life was measured by the EuroQol Health Questionnaire and Short-Form 12 Physical and Mental Component Scores at 6 and 12 months. Multiple logistic and linear regression with generalized estimating equations was used to explore the association between frailty and health-related quality of life. In total, frailty was diagnosed in 33% (95% CI, 28-38). Frail patients were older, had more comorbidities, and higher illness severity. EuroQol-visual analogue scale scores were lower for frail compared with not frail patients at 6 months (52.2 ± 22.5 vs 64.6 ± 19.4; p < 0.001) and 12 months (54.4 ± 23.1 vs 68.0 ± 17.8; p < 0.001). Frail patients reported greater problems with mobility (71% vs 45%; odds ratio, 3.1 [1.6-6.1]; p = 0.001), self-care (49% vs 15%; odds ratio, 5.8 [2.9-11.7]; p < 0.001), usual activities (80% vs 52%; odds ratio, 3.9 [1.8-8.2]; p < 0.001), pain/discomfort (68% vs 47%; odds ratio, 2.0 [1.1-3.8]; p = 0.03), and anxiety/depression (51% vs 27%; odds ratio, 2.8 [1.5-5.3]; p = 0.001) compared with not frail patients. Frail patients described lower health-related quality of life on both physical component score (34.7 ± 7.8 vs 37.8 ± 6.7; p = 0.012) and mental component score (33.8 ± 7.0 vs 38.6 ± 7.7; p < 0.001) at 12 months.

CONCLUSIONS

Frail survivors of critical illness experienced greater impairment in health-related quality of life, functional dependence, and disability compared with those not frail. The systematic assessment of frailty may assist in better informing patients and families on the complexities of survivorship and recovery.

Measuring and predicting long-term outcomes in older survivors of critical illness

Older adults (age ≥65 years) now initially survive what were previously fatal critical illnesses, but long-term mortality and disability after critical illness remain high. Most studies show that the majority of deaths among older ICU survivors occur during the first 6 to 12 months after hospital discharge. Less is known about the relationship between critical illness and subsequent cause of death, but longitudinal studies of ICU survivors of pneumonia, stroke, and those who require prolonged mechanical ventilation suggest that many debilitated older ICU survivors die from recurrent infections and sepsis. Recent studies of older ICU survivors have created a new standard for longitudinal critical care outcomes studies with a systematic evaluation of pre-critical illness comorbidities and disability and detailed assessments of physical and cognitive function after hospital discharge. These studies show that after controlling for pre-morbid health, older ICU survivors experience large and persistent declines in cognitive and physical function after critical illness. Long-term health-related quality-of-life studies suggest that some older ICU survivors may accommodate to a degree of physical disability and still report good emotional and social well-being, but these studies are subject to survivorship and proxy-response bias. In order to risk-stratify older ICU survivors for long-term (6-12 months) outcomes, we will need a paradigm shift in the timing and type of predictors measured. Emerging literature suggests that the initial acuity of critical illness will be less important, whereas prehospitalization estimates of disability and frailty, and, in particular, measures of comorbidity, frailty, and disability near the time of hospital discharge will be essential in creating reliable long-term risk-prediction models.

Predicting one-year mortality of critically ill patients with early acute kidney injury: data from the prospective multicenter FINNAKI study

INTRODUCTION

No predictive models for long-term mortality in critically ill patients with acute kidney injury (AKI) exist. We aimed to develop and validate two predictive models for one-year mortality in patients with AKI based on data (1) on intensive care unit (ICU) admission and (2) on the third day (D3) in the ICU.

METHODS

This substudy of the FINNAKI study comprised 774 patients with early AKI (diagnosed within 24 hours of ICU admission). We selected predictors a priori based on previous studies, clinical judgment, and differences between one-year survivors and non-survivors in patients with AKI. We validated the models internally with bootstrapping.

RESULTS

Of 774 patients, 308 (39.8%, 95% confidence interval (CI) 36.3 to 43.3) died during one year. Predictors of one-year mortality on admission were: advanced age, diminished premorbid functional performance, co-morbidities, emergency admission, and resuscitation or hypotension preceding ICU admission. The area under the receiver operating characteristic curve (AUC) (95% CI) for the admission model was 0.76 (0.72 to 0.79) and the mean bootstrap-adjusted AUC 0.75 (0.74 to 0.75). Advanced age, need for mechanical ventilation on D3, number of co-morbidities, higher modified SAPS II score, the highest bilirubin value by D3, and the lowest base excess value on D3 remained predictors of one-year mortality on D3. The AUC (95% CI) for the D3 model was 0.80 (0.75 to 0.85) and by bootstrapping 0.79 (0.77 to 0.80).

CONCLUSIONS

The prognostic performance of the admission data-based model was acceptable, but not good. The D3 model for one-year mortality performed fairly well in patients with early AKI.

Prediction model for critically ill patients with acute respiratory distress syndrome

BACKGROUND AND OBJECTIVES

Acute respiratory distress syndrome (ARDS) is a major cause respiratory failure in intensive care unit (ICU). Early recognition of patients at high risk of death is of vital importance in managing them. The aim of the study was to establish a prediction model by using variables that were readily available in routine clinical practice.

METHODS

The study was a secondary analysis of data obtained from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center. Patients were enrolled between August 2007 and July 2008 from 33 hospitals. Demographics and laboratory findings were extracted from dataset. Univariate analyses were performed to screen variables with p<0.3. Then these variables were subject to automatic stepwise forward selection with significance level of 0.1. Interaction terms and fractional polynomials were examined for variables in the main effect model. Multiple imputations and bootstraps procedures were used to obtain estimations of coefficients with better external validation. Overall model fit and logistic regression diagnostics were explored.

MAIN RESULT

A total of 282 ARDS patients were included for model development. The final model included eight variables without interaction terms and non-linear functions. Because the variable coefficients changed substantially after exclusion of most poorly fitted and influential subjects, we estimated the coefficient after exclusion of these outliers. The equation for the fitted model was: g(Χ)=0.06×age(in years)+2.23(if on vasopressor)+1.37×potassium (mmol/l)-0.007×platelet count (×109)+0.03×heart rate (/min)-0.29×Hb(g/dl)-0.67×T(°C)+0.01×PaO_2+13, and the probability of death π(Χ)=eg(Χ)/(1+eg(Χ)).

CONCLUSION

The study established a prediction model for ARDS patients requiring mechanical ventilation. The model was examined with rigorous methodology and can be used for risk stratification in ARDS patients.

High burden of palliative needs among older intensive care unit survivors transferred to post-acute care facilities. a single-center study

RATIONALE

Adults with chronic critical illness (tracheostomy after ≥ 10 d of mechanical ventilation) have a high burden of palliative needs, but little is known about the actual use and potential need of palliative care services for the larger population of older intensive care unit (ICU) survivors discharged to post-acute care facilities.

OBJECTIVES

To determine whether older ICU survivors discharged to post-acute care facilities have potentially unmet palliative care needs.

METHODS

We examined electronic records from a 1-year cohort of 228 consecutive adults ≥ 65 years of age who had their first medical-ICU admission in 2009 at a single tertiary-care medical center and survived to discharge to a post-acute care facility (excluding hospice). Use of palliative care services was defined as having received a palliative care consultation. Potential palliative care needs were defined as patient characteristics suggestive of physical or psychological symptom distress or anticipated poor prognosis. We examined the prevalence of potential palliative needs and 6-month mortality.

MEASUREMENTS AND MAIN RESULTS

The median age was 78 years (interquartile range, 71-84 yr), and 54% received mechanical ventilation for a median of 7 days (interquartile range, 3-16 d). Six subjects (2.6%) received a palliative care consultation during the hospitalization. However, 88% had at least one potential palliative care need; 22% had chronic wounds, 37% were discharged on supplemental oxygen, 17% received chaplaincy services, 23% preferred to not be resuscitated, and 8% were designated "comfort care." The 6-month mortality was 40%.

CONCLUSIONS

Older ICU survivors from a single center who required postacute facility care had a high burden of palliative care needs and a high 6-month mortality. The in-hospital postcritical acute care period should be targeted for palliative care assessment and intervention.

Platelet-monocyte aggregate formation and mortality risk in older patients with severe sepsis and septic shock

BACKGROUND

Aging-related changes in platelet and monocyte interactions may contribute to adverse outcomes in sepsis but remain relatively unexamined. We hypothesized that differential platelet-monocyte aggregate (PMA) formation in older septic patients alters inflammatory responses and mortality.

METHODS

We prospectively studied 113 septic adults admitted to the intensive care unit with severe sepsis or septic shock. Patients were dichotomized a priori into one of two groups: older (age ≥ 65 years, n = 28) and younger (age < 65 years, n = 85). PMA levels were measured in whole blood via flow cytometry within 24 hours of admission. Plasma levels of IL-6 and IL-8, proinflammatory cytokines produced by monocytes upon PMA formation, were determined by commercial assays. Patients were followed for the primary outcome of 28-day, all-cause mortality.

RESULTS

Elevated PMA levels were associated with an increased risk of mortality in older septic patients (hazard ratio for mortality 5.64, 95% confidence interval 0.64-49.61). This association remained after adjusting for potential confounding variables in multivariate regression. Receiver operating curve analyses demonstrated that PMA levels greater than or equal to 8.43% best predicted 28-day mortality in older septic patients (area under the receiver operating curve 0.82). Plasma IL-6 and IL-8 levels were also significantly higher in older nonsurvivors. In younger patients, neither PMA levels nor plasma monokines were significantly associated with mortality.

CONCLUSIONS

Increased PMA formation, and associated proinflammatory monokine synthesis, predicts mortality in older septic patients. Although larger studies are needed, our findings suggest that heightened PMA formation in older septic patients may contribute to injurious inflammatory responses and an increased risk of mortality.

The feasibility of measuring frailty to predict disability and mortality in older medical intensive care unit survivors

PURPOSE

To determine whether frailty can be measured within 4 days prior to hospital discharge in older intensive care unit (ICU) survivors of respiratory failure and whether it is associated with post-discharge disability and mortality.

MATERIALS AND METHODS

We performed a single-center prospective cohort study of 22 medical ICU survivors age 65 years or older who had received noninvasive or invasive mechanical ventilation for at least 24 hours. Frailty was defined as a score of ≥3 using Fried's 5-point scale. We measured disability with the Katz Activities of Daily Living. We estimated unadjusted associations between Fried's frailty score and incident disability at 1-month and 6-month mortality using Cox proportional hazard models.

RESULTS

The mean (SD) age was 77 (9) years, mean Acute Physiology and Chronic Health Evaluation II score was 27 (9.7), mean frailty score was 3.4 (1.3), and 18 (82%) were frail. Nine subjects (41%) died within 6 months, and all were frail. Each 1-point increase in frailty score was associated with a 90% increased rate of incident disability at 1-month (rate ratio: 1.9, 95% CI 0.7-4.9) and a threefold increase in 6-month mortality (rate ratio: 3.0, 95% CI 1.4-6.3).

CONCLUSIONS

Frailty can be measured in older ICU survivors near hospital discharge and is associated with 6-month mortality in unadjusted analysis. Larger studies to determine if frailty independently predicts outcomes are warranted.