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      Suicide and self-harm in adult survivors of critical illness: population based cohort study

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          Abstract

          Objective

          To analyse the association between survival from critical illness and suicide or self-harm after hospital discharge.

          Design

          Population based cohort study using linked and validated provincial databases.

          Setting

          Ontario, Canada between January 2009 and December 2017 (inclusive).

          Participants

          Consecutive adult intensive care unit (ICU) survivors (≥18 years) were included. Linked administrative databases were used to compare ICU hospital survivors with hospital survivors who never required ICU admission (non-ICU hospital survivors). Patients were categorised based on their index hospital admission (ICU or non-ICU) during the study period.

          Main outcome measures

          The primary outcome was the composite of death by suicide (as noted in provincial death records) and deliberate self-harm events after discharge. Each outcome was also assessed independently. Incidence of suicide was evaluated while accounting for competing risk of death from other causes. Analyses were conducted by using overlap propensity score weighted, cause specific Cox proportional hazard models.

          Results

          423 060 consecutive ICU survivors (mean age 61.7 years, 39% women) were identified. During the study period, the crude incidence (per 100 000 person years) of suicide, self-harm, and the composite of suicide or self-harm among ICU survivors was 41.4, 327.9, and 361.0, respectively, compared with 16.8, 177.3, and 191.6 in non-ICU hospital survivors. Analysis using weighted models showed that ICU survivors ( v non-ICU hospital survivors) had a higher risk of suicide (adjusted hazards ratio 1.22, 95% confidence interval 1.11 to 1.33) and self-harm (1.15, 1.12 to 1.19). Among ICU survivors, several factors were associated with suicide or self-harm: previous depression or anxiety (5.69, 5.38 to 6.02), previous post-traumatic stress disorder (1.87, 1.64 to 2.13), invasive mechanical ventilation (1.45, 1.38 to 1.54), and renal replacement therapy (1.35, 1.17 to 1.56).

          Conclusions

          Survivors of critical illness have increased risk of suicide and self-harm, and these outcomes were associated with pre-existing psychiatric illness and receipt of invasive life support. Knowledge of these prognostic factors might allow for earlier intervention to potentially reduce this important public health problem.

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          Most cited references39

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          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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            Sensitivity Analysis in Observational Research: Introducing the E-Value.

            Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This article introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The authors propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
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              New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality.

              The ICD-9-CM adaptation of the Charlson comorbidity score has been a valuable resource for health services researchers. With the transition into ICD-10 coding worldwide, an ICD-10 version of the Deyo adaptation was developed and validated using population-based hospital data from Victoria, Australia. The algorithm was translated from ICD-9-CM into ICD-10-AM (Australian modification) in a multistep process. After a mapping algorithm was used to develop an initial translation, these codes were manually examined by the coding experts and a general physician for face validity. Because the ICD-10 system is country specific, our goal was to keep many of the translated code at the three-digit level for generalizability of the new index. There appears to be little difference in the distribution of the Charlson Index score between the two versions. A strong association between increasing index scores and mortality exists: the area under the ROC curve is 0.865 for the last year using the ICD-9-CM version and remains high, at 0.855, for the ICD-10 version. This work represents the first rigorous adaptation of the Charlson comorbidity index for use with ICD-10 data. In comparison with a well-established ICD-9-CM coding algorithm, it yields closely similar prevalence and prognosis information by comorbidity category.
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                Author and article information

                Contributors
                Role: critical care fellow
                Role: research coordinator
                Role: associate professor
                Role: analyst
                Role: methodologist
                Role: public health physician
                Role: professor
                Role: professor
                Role: associate professor
                Role: distinguished professor
                Role: professor
                Role: professor
                Role: professor
                Role: professor
                Role: professor
                Role: critical care physician
                Role: health services researcher
                Role: assistant professor
                Role: assistant professor
                Journal
                BMJ
                BMJ
                BMJ-UK
                bmj
                The BMJ
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2021
                05 May 2021
                : 373
                : n973
                Affiliations
                [1 ]Division of Critical Care, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
                [2 ]Department of Emergency Medicine, University of Ottawa, Ottawa, ON, Canada
                [3 ]ICES, Toronto, ON, Canada
                [4 ]Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada
                [5 ]School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
                [6 ]Bruyère Research Institute, Ottawa, ON, Canada
                [7 ]Division of Nephrology, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
                [8 ]Department of Family Medicine, University of Ottawa, Ottawa, ON, Canada
                [9 ]Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada
                [10 ]Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
                [11 ]Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
                [12 ]Department of Medicine, Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
                [13 ]Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, USA
                [14 ]Department of Medicine, Division of Critical Care, McMaster University, Hamilton, ON, Canada
                [15 ]Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
                [16 ]Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
                [17 ]Li Ka Shing Knowledge Institute, St Michael’s Hospital, Toronto, ON, Canada
                [18 ]Department of Psychiatry and Behavioural Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
                [19 ]Intensive Care National Audit and Research Centre, Napier House, London, UK
                [20 ]Department of Critical Care, Queensway Carleton Hospital, Ottawa, ON, Canada
                [21 ]Division of Palliative Care, Department of Medicine, University of Ottawa, Ottawa, ON, Canada
                [22 ]Institut du Savoir Montfort, Ottawa, ON, Canada
                Author notes
                Correspondence to: S M Fernando, Department of Critical Care, The Ottawa Hospital, General Campus, 501 Smyth Rd, Ottawa, ON, Canada K1H 8M2 sfernando@ 123456qmed.ca (or @shanfernands on Twitter)
                Author information
                https://orcid.org/0000-0003-4549-4289
                Article
                fers064842
                10.1136/bmj.n973
                8097311
                33952509
                8ca75d5e-894b-4930-bf8d-f6bb6fb455dc
                © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 07 April 2021
                Categories
                Research

                Medicine
                Medicine

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