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Survivors of critical illness frequently require increased healthcare resources after hospital discharge. We undertook a systematic review and meta-analysis to assess hospital re-admission rates following critical care admission and to explore potential re-admission risk factors. We searched the MEDLINE, Embase and CINAHL databases on 05 March 2020. Our search strategy incorporated controlled vocabulary and text words for hospital re-admission and critical illness, limited to the English language. Two reviewers independently applied eligibility criteria and assessed quality using the Newcastle Ottawa Score checklist and extracted data. The primary outcome was acute hospital re-admission in the year after critical care discharge. Of the 8851 studies screened, 87 met inclusion criteria and 41 were used within the meta-analysis. The analysis incorporated data from 3,897,597 patients and 741,664 re-admission episodes. Pooled estimates for hospital re-admission after critical illness were 16.9% (95%CI: 13.3-21.2%) at 30 days; 31.0% (95%CI: 24.3-38.6%) at 90 days; 29.6% (95%CI: 24.5-35.2%) at six months; and 53.3% (95%CI: 44.4-62.0%) at 12 months. Significant heterogeneity was observed across included studies. Three risk factors were associated with excess acute care rehospitalisation one year after discharge: the presence of comorbidities; events during initial hospitalisation (e.g. the presence of delirium and duration of mechanical ventilation); and subsequent infection after hospital discharge. Hospital re-admission is common in survivors of critical illness. Careful attention to the management of pre-existing comorbidities during transitions of care may help reduce healthcare utilisation after critical care discharge. Future research should determine if targeted interventions for at-risk critical care survivors can reduce the risk of subsequent rehospitalisation., (© 2021 The Authors. Anaesthesia published by John Wiley & Sons Ltd on behalf of Association of Anaesthetists.)

The capacity to measure the impact of an intervention on long-term functional outcomes might be improved if research methodology reflected our clinical approach, which is to individualise goals of care to what is achievable for each patient. The objective of this multicentre inception cohort study was to evaluate the feasibility of rapidly and accurately categorising patients, who were eligible for simulated enrolment into a clinical trial, into unique categories based on premorbid function. Once a patient met eligibility criteria a rapid 'baseline assessment' was conducted to categorise patients into one of eight specified groups. A subsequent 'gold standard' assessment was made by an independent blinded assessor once patients had recovered sufficiently to allow such an assessment to occur. Accuracy was predefined as agreement in >80% of assessments. One hundred and twenty-two patients received a baseline assessment and 104 (85%) were categorised to a unique category. One hundred and six patients survived to have a gold standard assessment performed, with 100 (94%) assigned to a unique category. Ninety-two patients had both a baseline and gold standard assessment, and these agreed in 65 (71%) patients. It was not feasible to rapidly and accurately categorise patients according to premorbid function.

Context: Hospice terminal care is now used by 10% to 15% of elderly Americans at variable points before their deaths., Objective: By examining the duration of patient survival after enrollment in hospice care, we sought to identify individual and market factors associated with the timing of hospice use., Design: We linked Medicare claims, census information, and Area Resource File data to form a national cohort of 151,410 hospice patients admitted in 1993 and followed up until late 1996. We examined this cohort with Cox regression and other means., Main Outcome Measure: The primary outcome measure was survival after hospice enrollment., Results: The patients had a mean +/- SD age of 79.0 +/- 7.4 years; 10.2% were nonwhite; 51.4% were female; and 71.3% had cancer. Median survival after hospice enrollment was 30 days (interquartile range, 10-86 days). After adjustment for measured patient, provider, and market factors, several variables were associated with relatively earlier hospice enrollment, farther from death. Compared with complementary groups, nonwhites were enrolled in hospice 4 days earlier; women, 5 days earlier; older people, 1 day earlier; and those with substance abuse, psychiatric disease, or dementia, each 3 days earlier. After adjustment, income and education were not associated with the timing of enrollment. Patients residing in markets with more hospital beds, greater hospice capacity, or a higher proportion of generalists were enrolled earlier., Conclusions: Even after adjustment for certain clinical attributes, individual social factors and local market factors were associated with survival after hospice enrollment. Certain socially disadvantaged groups were enrolled earlier, as were those residing in areas with more medical institutions. The decision to enroll patients in hospice may depend on both nonclinical and clinical factors.

Background: The Charlson Score is a particularly popular form of comorbidity adjustment in claims data analysis. However, the effects of certain implementation decisions have not been empirically examined., Objective: To determine the effects of alternative data sources and lookback periods on the performance of Charlson scores in the prediction of mortality following hospitalization., Subjects: A representative sample of 1,387 elderly patients hospitalized in 1993, drawn from the Medicare Current Beneficiary Survey (MCBS). Three years of linked Medicare claims and survey instruments were available for all patients, as was 2-year mortality follow-up., Statistical Methods: Nested Cox regression and comparisons of areas under the Receiver Operating Characteristic (ROC) curve were used to evaluate ability to predict mortality., Results: Compared with a 1-year lookback involving solely inpatient claims, statistically and empirically significant improvements in the prediction of mortality are obtained by incorporating alternative sources of data (particularly 2 years of inpatient data and 1 year of outpatient and auxiliary claims), but only if indices derived from distinct sources of data are entered into the regression distinctly. The area under the ROC curve for 1-year mortality predication increases from 0.702 to 0.741 (P = 0.002). Furthermore, these improvements in explanatory power obtained whether one also controls for Charlson scores based on self-reported health history and/or secondary diagnoses from the claim for the index hospitalization itself. Finally, claims-based comorbidity adjustment performs comparably to survey-derived adjustment, with areas under the ROC curve of 0.702 and 0.704, respectively., Conclusions: The widespread practice of comorbidity adjustment in pre-existing administrative data sources can be improved by taking more complete advantage of existing administrative data sources.

Study Objective: Cardiorespiratory resuscitation (CPR) nonprovision-the failure of bystanders to provide CPR to cardiac arrest victims-remains a well-documented public health problem associated with significant mortality. Multivariate data on failure to provide CPR are limited. Given the established independent contributions of neighborhoods to explaining many behaviors, we asked the following questions: Do neighborhood characteristics affect the likelihood of CPR nonprovision? In particular, we sought to identify the characteristics of areas that have had the most success in providing CPR., Methods: We performed multivariable logistic regression analysis of a prospectively collected cohort of 4,379 cardiac arrests linked at an individual level to neighborhood data from the US Census. These arrests represent all out-of-hospital cardiac arrests in the City of Chicago in 1987 and 1988., Results: In multivariate analysis, patients who had cardiac arrests who lived in neighborhoods where cardiac arrests were more common were significantly more likely to receive CPR. Patients with arrests in racially integrated neighborhoods were most likely to be provided with CPR, followed by those in predominately white neighborhoods, with the lowest rates of CPR provision in predominately black neighborhoods. Neither the socioeconomic status, number of elderly, nor the occupational characteristics of the neighborhood appeared to influence CPR provision. At the individual level, in-home arrests and arrests among middle-aged black residents (relative to older black and all white residents) were less likely to receive CPR., Conclusion: Substantial variation in rates of CPR nonprovision exists between neighborhoods; the variation is associated with neighborhood characteristics. Combining individual and neighborhood data allows identification of important factors associated with the failure to provide CPR.

Background: Since prognostication appears increasingly important in clinical practice, especially in end-of-life care, we examined physicians' experiences and attitudes regarding it., Methods: We mailed a survey to a national sample of 1311 internists, yielding 697 responses that were analyzed with multivariate models and other means. Findings were supplemented by qualitative comments from 162 physicians and by interviews with 20., Results: On an annual basis, the typical internist addressed the question "How long do I have to live?" 10 times, withdrew or withheld life support 5 times, and referred 5 patients to hospice. Nevertheless, physicians disdain prognostication: 60.4% find it "stressful" to make predictions; 58.7% find it "difficult"; 43.7% wait to be asked by a patient before offering predictions; 80.2% believe patients expect too much certainty; 50.2% believe that if they were to make an error, patients might lose confidence; 89.9% believe they should avoid being too specific; and 56.8% report inadequate training in prognostication. With respect to the key concept of "terminal" illness, physicians on average believe that such patients should have 13.5+/-11.8 weeks to live, but responses varied substantially from 1 to 75 weeks., Conclusions: Physicians (1) commonly encounter situations that require prognostication, (2) feel poorly prepared for prognostication, (3) find it stressful and difficult to make predictions, (4) believe that patients expect too much certainty and might judge them adversely for prognostic errors, and (5) vary in how they regard the key concept of being "terminally ill." These observations may have significant consequences for patient care.

We describe a method for the development of cohorts of up to three quarters of the 14 million married couples aged 65 and over in the United States. The health care experiences, illness histories, and mortality of these identified couples can be assessed longitudinally using Medicare data. We summarize strengths and limitations of using data from Medicare administrative records for the study of marriage, health, and aging. We illustrate the method by demonstrating substantial differences in survival in a cohort of hospice patients as a function of not only the patient's own diagnosis and illness burden but also the patient's spouse's illness burden.

Systematic, nationally representative information about physicians' attitudes and behavior with respect to hospice care is not available. We sought to describe these previously unexamined attitudes and practices. We conducted a mail survey of a random national sample of 1311 internists, of whom 697 responded (an unadjusted response rate of 53%). We elicited physician's attitudes and self-reported practice with respect to hospice. Most internists (89%) felt that hospice care was a good form of terminal care. Physicians with longer definitions of what constitutes "terminal" illness and those whose patients were more accepting of their prognoses were more likely to hold this opinion. Over the course of a year, the median internist referred five patients to hospice. Specialists and those with longer definitions of "terminal" were more likely to have done so. When asked "If you knew exactly how long a patient had to live, how long before death would you refer them to hospice?" the average response was 12.1 +/- 8.5 weeks, but responses varied from 1 to 52 weeks, and the pattern of responses was bimodal, with one peak at about 13 weeks (73% of internists) and one at about 25 weeks (27%). Moreover, the distribution of hospice enrollment times implied by physician reports about ideal practice is significantly more compact than the distribution of survival that is actually observed; physicians support far fewer very short (<2 weeks) and very long (>6 months) stays in hospice than are observed. We conclude that internists show significant support for, and utilization of, hospice and they endorse a length of stay that is longer than currently observed. These findings suggest that it may be possible to increase both the number of patients using hospice and their duration of use of hospice.

Patients' families are important in hospice not only because they themselves might benefit from it, but also because they influence whether and how hospice is used to begin with. We hypothesized that, among the married, people with less capable spouses (in this case, those who are less healthy) should be less able to use home hospice care and should enroll in hospice later in the course of their illness, closer to death. We tested this hypothesis by evaluating the impact of spousal health on the timing of patients' use of hospice in a select group of 517 couples, a total of 1034 patients, for whom we have health data for both partners from Medicare claims records. We evaluated cohort survival using the Kaplan-Meier method and Cox regression, and we measured illness burden using the Charlson comorbidity score. Among the probands, 45.6% had a primary cancer diagnosis, 44.5% were female, 7.5% were nonwhite, their mean age was 80.0 years (+/-7.6 SD), and their mean Charlson score was 3.1 +/- 3.0. The probands' median survival after hospice enrollment was 55 days. Spouses were similarly composed demographically, but were less likely to have a cancer primary diagnosis (35.4% had cancer) and were slightly younger than the probands; their mean Charlson score was 2.9 +/- 2.9. Each additional point in the Charlson score of the spouse, controlling for measured characteristics of the proband, is associated with a 5.1% increase in the risk of death after enrollment. That is, after controlling for the characteristics of hospice patients themselves, those with sicker spouses are enrolled later. Compared with patients with the healthiest spouses, those with moderately sick and very sick spouses had considerably later enrollment into hospice, with the median survival decreasing from 111 to 49 to 22 days across the groups. Our data suggest that there may be a separate role of spousal health in affecting the time of hospice enrollment, and that patients' social support may affect not just their health status to begin with, but also their use of health care in general.