Multimorbidity and Its Relationship With Long-Term Outcomes After Critical Care Discharge

Abstract

BackgroundSurvivors of critical illness have poor long-term outcomes with subsequent increases in health care utilization. Less is known about the interplay between multimorbidity and long-term outcomes.Research QuestionHow do baseline patient demographics impact mortality and health care utilization in the year after discharge from critical care?Study Design and MethodsUsing data from a prospectively collected cohort, we used propensity score matching to assess differences in outcomes between patients with a critical care encounter and patients admitted to the hospital without critical care. Long-term mortality was examined via nationally linked data as was hospital resource use in the year after hospital discharge. The cause of death was also examined.ResultsThis analysis included 3,112 participants. There was no difference in long-term mortality between the critical care and hospital cohorts (adjusted hazard ratio, 1.09; 95% CI, 0.90-1.32; P = .39). Prehospitalization emotional health issues (eg, clinical diagnosis of depression) were associated with increased long-term mortality (hazard ratio, 1.49; 95% CI, 1.14-1.96; P < .004). Health care utilization was different between the two cohorts in the year after discharge with the critical care cohort experiencing a 29% increased risk of hospital readmission (OR, 1.29; 95% CI, 1.11-1.50; P = .001).InterpretationThis national cohort study has demonstrated increased resource use for critical care survivors in the year after discharge but fails to replicate past findings of increased longer-term mortality. Multimorbidity, lifestyle factors, and socioeconomic status appear to influence long-term outcomes and should be the focus of future research. Survivors of critical illness have poor long-term outcomes with subsequent increases in health care utilization. Less is known about the interplay between multimorbidity and long-term outcomes. How do baseline patient demographics impact mortality and health care utilization in the year after discharge from critical care? Using data from a prospectively collected cohort, we used propensity score matching to assess differences in outcomes between patients with a critical care encounter and patients admitted to the hospital without critical care. Long-term mortality was examined via nationally linked data as was hospital resource use in the year after hospital discharge. The cause of death was also examined. This analysis included 3,112 participants. There was no difference in long-term mortality between the critical care and hospital cohorts (adjusted hazard ratio, 1.09; 95% CI, 0.90-1.32; P = .39). Prehospitalization emotional health issues (eg, clinical diagnosis of depression) were associated with increased long-term mortality (hazard ratio, 1.49; 95% CI, 1.14-1.96; P < .004). Health care utilization was different between the two cohorts in the year after discharge with the critical care cohort experiencing a 29% increased risk of hospital readmission (OR, 1.29; 95% CI, 1.11-1.50; P = .001). This national cohort study has demonstrated increased resource use for critical care survivors in the year after discharge but fails to replicate past findings of increased longer-term mortality. Multimorbidity, lifestyle factors, and socioeconomic status appear to influence long-term outcomes and should be the focus of future research. FOR EDITORIAL COMMENT, SEE PAGE 1587The number of admissions to critical care internationally continues to steadily increase every year.1Intensive Care National Audit and Research CentreKey statistics from the case mis programme- adult, general critical care units.https://www.icnarc.org/DataServices/Attachments/Download/fca008ac-9216-ea11-911e-00505601089bGoogle Scholar,2Halpern N.A. Pastores S.M. Critical care medicine in the United States 200-2005: an analysis of bed numbers, occupancy rates, payer mix and costs.Crit Care Med. 2010; 38: 65-71Crossref PubMed Scopus (579) Google Scholar Evidence demonstrates that discharge from critical care is often the start of a challenging recovery trajectory for both patients and caregivers, including physical, social, cognitive, and emotional problems in the years after discharge.3McPeake J.M. Boehm L.M. Hibbert E.B. et al.Key components of ICU recovery programs: what did patients report provided benefit?.Crit Care Explor. 2020; 2e0088Crossref PubMed Google Scholar, 4Herridge M.S. Cheung A.M. Tansey C.M. et al.One-year outcomes in survivors of the acute respiratory distress syndrome.N Engl J Med. 2003; 348: 683-693Crossref PubMed Scopus (1537) Google Scholar, 5Wade D.M. Howell D.C. Weinman J.A. et al.Investigating risk factors for psychological morbidity three months after intensive care: a prospective study.Crit Care. 2012; 16: R192Crossref PubMed Scopus (149) Google Scholar FOR EDITORIAL COMMENT, SEE PAGE 1587 Excess mortality and increased health care utilization have also been reported in the post-hospital period.6Lone N.I. Gillies M.A. Haddow C. et al.Five-year mortality and hospital costs associated with surviving intensive care.Am J Respir Crit Care Med. 2016; 194: 198-208Crossref PubMed Scopus (101) Google Scholar,7Lone N.I. Lee R. Salisbury L. et al.Predicting risk of unplanned hospital readmission in survivors of critical illness: a population level cohort study.Thorax. 2019; 74: 1046-1054Crossref PubMed Scopus (18) Google Scholar Patients who have been diagnosed with sepsis and those with worse pre-critical care physical health are particularly at risk of poorer outcomes; however, such excess long-term mortality was not present for hypoxic respiratory failure.8Prescott H.C. Osterholzer J.J. Langa K.M. et al.Late mortality after sepsis: propensity matched cohort study.BMJ. 2016; 353: I2375Crossref PubMed Scopus (163) Google Scholar, 9Shankar-Hari M. Harrison D. Ferrando-Vivas P. et al.Risk factors at index hospitalisation associated with longer-term mortality in adult sepsis survivors.JAMA Netw Open. 2019; 2e194900Crossref PubMed Scopus (27) Google Scholar, 10Prescott H.C. Sjoding M.W. Langa K.M. et al.Late mortality after acute hypoxic respiratory failure.Thorax. 2018; 73: 618-625Crossref Scopus (15) Google Scholar However, beyond these average population effects, there is limited information about the variation in patients based on their preexisting social circumstances and mental health problems, and the impact that these may have on longer-term outcomes. Using data from the UK Biobank, we sought to advance the evidence by answering the following three questions: (1) do critical care patients have a different mortality rate or readmission risk use in comparison with hospitalized patients who do not need care in a critical care environment?; (2) what are the causes of death in the post-critical care period?; and (3) what is the interplay between mental and social health issues and health care utilization after admission to critical care? We reported an observational cohort study as per the STrengthening the Reporting of OBservational studies in Epidemiology guidelines.11Von Elm E. Altman D.G. Egger M. et al.Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies.BMJ. 2007; 335: 806-808Crossref PubMed Google Scholar Data were obtained from the UK Biobank, a large prospective health resource for research which aims to improves the prevention, diagnosis, and treatment of a range of illnesses. Between 2006 and 2010, the UK Biobank recruited > 500,000 participants from the UK population.12Sudlow C. Gallacher J. Allen N. et al.UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.PLoS Med. 2015; 12e1001779Crossref PubMed Scopus (2334) Google Scholar Those participants enrolled and attended assessment centers across the United Kingdom, where they completed a wide range of assessments alongside in-depth objective physical measurement. The UK Biobank study was approved by the North West Multicentre Ethics Research Committee; participants provided written informed consent and agreed to have their health followed longitudinally, via linkage to routine clinical data (including health care resource use and diagnostic data). This study is part of UK Biobank project 57617 (NHS National Research Ethics Service No. 11/NW/0382). Data for this analysis were extracted from the UK Biobank server on October 22, 2020 (censor date). All patients who have withdrawn consent from the UK Biobank were removed from this analysis. Data in the UK Biobank are linked to routinely held NHS data annually. Two study cohorts were created from the UK Biobank (Fig 1). The primary cohort in this analysis included participants with a critical care admission who had UK Biobank data available before critical care admission (critical care cohort). The critical care cohort was defined by consultant specialty within the UK Biobank dataset (e-Table 1). We considered the first critical care and first hospital admission for the comparison cohorts to accurately reflect baseline status. We used data from the immediate, preceding UK Biobank assessment center visit to the admission. The second cohort was a group of hospitalized patients with similar baseline characteristics not admitted to critical care (hospital cohort). Only participants ≥ 18 years of age at the time of their critical care/hospital admission, who had been admitted to hospital for ≥ 1 day, were included. Participants who had a previous cancer diagnosis (solid tumor without metastasis, malignancy [including lymphoma and leukemia except malignant neoplasm of skin], lymphoma, metastatic cancer or metastatic solid tumor) were excluded from both cohorts because patients with cancer are known to have a different recovery trajectory than patients without cancer after critical care discharge.13Puxty K. McLoone P. Quasim T. et al.Characteristics and outcomes of surgical patients with solid cancers admitted to the intensive care unit.JAMA Surg. 2018; 153: 834-840Crossref PubMed Scopus (16) Google Scholar This exclusion included a diagnosis of cancer before or during the index admission. Participants in this analysis were admitted to critical care between 2006 and 2017. Area-level socioeconomic deprivation was assessed by the Townsend Deprivation Index, corresponding to the output area in which the respondent’s home postcode was recorded.14UK Data Service Census Data. 2011 UK Townsend Deprivation Score. Accessed October 24, 2020. https://statistics.ukdataservice.ac.uk/dataset/2011-uk-townsend-deprivation-scoresGoogle Scholar Comorbidities were classified using the Elixhauser Comorbidity Index and the Charlson Comorbidity Index.15Elixhauser A. Steiner C. Harris D.R. Coffey R.M. Comorbidity measures for use with administrative data.Med Care. 1998; 36: 8-27Crossref PubMed Scopus (5992) Google Scholar,16Sundararajan V. Henderson T. Perry C. et al.New ICD-10 version of the Charlson comorbidity index predicted in hospital mortality.J Clin Epidemiol. 2004; 57: 1288-1294Abstract Full Text Full Text PDF PubMed Scopus (1209) Google Scholar A clinical diagnosis of depression was included as a comorbidity within the Elixhauser Comorbidity Index. Comorbidities were included in this analysis if they had been diagnosed before or at the hospital/critical care admission. Furthermore, only comorbidities which had been diagnosed during an acute hospital encounter were included, with the aim that this study characterized the clinically important comorbidities. Comorbidities are integrated into the UK Biobank via routinely collected national hospital data. The comorbidities used are listed in e-Table 2. Ethnicity was also recorded. Because educational attainment has been shown to be important during recovery from critical illness, we included it in our analysis.17Marra A. Pandharipande P.P. Girad T.D. et al.Co-occurrence of post-intensive care syndrome problems among survivors of critical Care Med. 2018; PubMed Scopus Google Scholar We to use propensity score matching on critical care using a to a cohort with in the J.M. for in observational studies using based on the propensity a for 2019; Scholar A of of the of the of the propensity score was used for the of matching to an was at a using age at hospital/critical care admission surgical [including first surgical if and hospital of the Townsend Index, educational attainment of the of multimorbidity or from e-Table and between UK Biobank assessment visit and admission to critical care. we also considered data which were obtained from the UK Biobank assessment center for participants before the index critical care/hospital admission. Only data for the preceding UK Biobank assessment center visit to critical care/hospital were This included the of of and social on these were has been with available in e-Table J.M. Henderson P. et following critical care: a prospective cohort study of UK Biobank Health Full Text Full Text PDF PubMed Scopus (3) Google Scholar between the two cohorts were using the or a were before The primary was mortality during information for both cohorts was obtained from the of death which is available via the UK The primary cause of death was obtained via linkage to national via UK using the of data are within the UK Data on mortality were available from the UK Biobank 2020. We also of readmission to hospital in the year after critical care discharge. We with All were via a using matching with the by with was on Data on are in e-Table of with P were considered to We for the critical care and the hospital by matched were used to hazard for mortality in the critical care hospital for were from the and M. et of Critical Care on and of long-term after critical Care Med. 2020; PubMed Scopus Google A. et in ICU Research from to a of Care Med. 2016; PubMed Scopus Google Scholar and the were both using from the hazard with P < using on were in the The were as with a corresponding 95% and range Because of the large number of participants with preexisting comorbidities included, we a analysis which the number of comorbidities to critical care longer-term mortality. We defined multimorbidity has two or comorbidities. for death was as We examined the primary cause of death from the cohorts using death and examined the of We rate using to the number of hospital admissions in the year post-critical care discharge. were included in the similar to the for the mortality analysis. were used to the risk of to hospital within 1 year of discharge. The were in of the with a corresponding 95% A readmission was defined as a hospital of > the UK Biobank participants had a Biobank assessment visit before a critical care admission. We were to this critical care group with participants with a care (Fig 1). The differences between the matched and cohorts are in the critical care cohort, the age was years were were and had two or comorbidities. The time from the UK Biobank assessment to admission to critical care was The critical care of was the hospital cohort, the age was years were and had two or comorbidities. 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There was no difference in mortality between the critical care participants with comorbidity in comparison with those with no comorbidities 95% CI, P = (Fig a we the impact of data on the with data of > did not the reported difference in mortality between the two cohorts (e-Table We examined the cause of death in both cohorts after post-hospital discharge. There were in the hospital cohort in the critical care cohort (e-Table There were no clinical differences in cause of death across the two cohorts after discharge (Fig the year after critical care/hospital the number of patients with at readmission to hospital in the critical care cohort was than the hospital cohort P < The number of was also in the critical care cohort P < The rate of hospital readmission was in the critical care cohort than the hospital cohort 95% CI, P < .001). had 95% CI, P = than those who had Participants with comorbidities (eg, also had (e-Table the critical care cohort had a 29% increased risk of a hospital readmission than the hospital cohort in the year after discharge (OR, 1.29; 95% CI, 1.11-1.50; P = (e-Table The of readmission in the first post-hospital discharge However, the critical care cohort to have an increased of during the This study the of a nationally linked cohort Using data from the UK Biobank, we have demonstrated that the long-term mortality of critical care participants was no different from a propensity matched hospital cohort, after for baseline physical, social, and emotional health status. with previous in the year after hospital discharge were in the critical care research has shown from sepsis and critical care is associated with long-term N.I. Gillies M.A. Haddow C. et al.Five-year mortality and hospital costs associated with surviving intensive care.Am J Respir Crit Care Med. 2016; 194: 198-208Crossref PubMed Scopus (101) Google H.C. Osterholzer J.J. Langa K.M. et al.Late mortality after sepsis: propensity matched cohort study.BMJ. 2016; 353: I2375Crossref PubMed Scopus (163) Google Scholar The in this study may be by the use of socioeconomic and mental health which have not been used we were also to access data on The influence of social and social on the risk of death are with risk factors for mortality (eg, and may the influence of risk factors (eg, physical J. and mortality a Med. 2010; Scopus Google Scholar The between socioeconomic status and and long-term mortality after critical illness has also been J. and health outcomes following critical illness: a Care Med. 2019; PubMed Scopus Google Scholar This study these important the social circumstances in which are important to recovery from critical care and should be both during and after the critical care encounter and mental health A of this study was the of depression as a clinically diagnosed than a Using this we have demonstrated that those patients with a of clinical depression before hospital/critical care have worse long-term outcomes in comparison with those without this The of physical and mental health is patients with mental health problems are to physical health mental health problems in those with long-term physical J. P. et patients with physical and mental 2012; PubMed Scopus Google Scholar Furthermore, mental health issues complex in multimorbidity and as socioeconomic deprivation K. M. et of multimorbidity and for and a 2012; Full Text Full Text PDF PubMed Scopus Google Scholar the associated with mental health and may not to critical care there may be to which influence outcomes and long-term patient the information we during and after the critical care should be and for patients and of Health defined as the and social which and in to access to and use information in which and is known to be in from and Health for health Scholar and patients and with and information about their health status during and after critical illness, we may in and outcomes. we also that we are not preexisting mental health a study demonstrated that were during critical P. Quasim T. C. et problems in intensive care from a 2019; Scopus Google Scholar that mental health and social comorbidities are the as physical comorbidities during the acute illness research has demonstrated that a critical care admission may a in those with M. in the intensive care intensive care admission a Care Med. PubMed Scopus Google Scholar and as during the critical care admission may have A by which critical care multimorbidity and the of deprivation is the of ICU which health and social care and social problems are in the ICU recovery as including social and in ICU and preexisting C. et after acute respiratory distress A national cohort study.Crit Care Med. 2020; PubMed Scopus Google Scholar, J. Quasim T. et to after critical illness and with a and 2019; 16: PubMed Scopus Google Scholar, J.M. Henderson P. et and problems of ICU survivors by a Care. 2019; PubMed Scopus Google Scholar The of or with critical care may also the impact of and social et in critical care: a Care Med. 2018; PubMed Scopus Google Scholar UK Biobank participants admitted to critical care had a 29% increased risk of a hospital admission in the year after critical care discharge. evidence has also shown the impact of issues (eg, socioeconomic alongside issues (eg, poor discharge on unplanned hospital E. Salisbury L. N.I. et hospital readmission among critical care a study of patients and 2018; PubMed Scopus Google Scholar Research is to to in the and to acute care. The UK Biobank has provided an to the impact of multimorbidity on longer-term mortality and health care utilization. The of this study are use of care which were used to longer-term mortality and health care utilization. However, this study have we have information on admission and consultant we have limited data about the of illness of participants and critical care illness because these data were not we examined comorbidities diagnosed before and at the index admission from routine data we do not the of these comorbidities or if they were at the time of admission. the time between UK Biobank assessment and critical care admission was The health of participants have during this time the to the UK Biobank was only The characteristics of this group may from the a large of the patients included in this and the UK Biobank as a were surgical the outcomes in this may be different for critical care > of these patients were to be be these because they do not reflect admissions to critical care. participants of the UK Biobank are on average and are to in than the general UK which may the et and of and depression in the UK 2020; PubMed Scopus Google Scholar for physical, social, and emotional long-term mortality in critical care patients not appear to be different in comparison with a hospital However, health care utilization was different between the two cohorts in the year after with the critical care cohort a rate of Multimorbidity, lifestyle factors, and socioeconomic status were associated with both increased mortality and health utilization and should be the focus of future critical care patients have a different mortality rate or readmission risk in comparison with hospitalized patients who do not need care in a critical care Using data from > we have demonstrated that critical care patients are to be to hospital in the year after discharge with a matched hospital However, there was no difference in long-term mortality between the critical care and hospital This cohort study has demonstrated increased resource use for critical care survivors in the year after but not increased mortality. critical care patients have a different mortality rate or readmission risk in comparison with hospitalized patients who do not need care in a critical care Using data from > we have demonstrated that critical care patients are to be to hospital in the year after discharge with a matched hospital However, there was no difference in long-term mortality between the critical care and hospital This cohort study has demonstrated increased resource use for critical care survivors in the year after but not increased mortality. J. M. and M. for the of this and as for the data. J. M. and M. the for the and the analysis. J. M. and T. J. to the of the All the for and approved the version of the The data that the findings of this study are available from the UK Biobank but to their of The had no in the study in the and of in the of the and in the to the for This research was and by the of the The and be in the of the with with

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