Multimorbidity states associated with higher mortality rates in organ dysfunction and sepsis: a data-driven analysis in critical care

Abstract

Background Sepsis remains a complex medical problem and a major challenge in healthcare. Diagnostics and outcome predictions are focused on physiological parameters with less consideration given to patients’ medical background. Given the aging population, not only are diseases becoming increasingly prevalent but occur more frequently in combinations (“multimorbidity”). We hypothesized the existence of patient subgroups in critical care with distinct multimorbidity states. We further hypothesize that certain multimorbidity states associate with higher rates of organ failure, sepsis, and mortality co-occurring with these clinical problems. Methods We analyzed 36,390 patients from the open source Medical Information Mart for Intensive Care III (MIMIC III) dataset. Morbidities were defined based on Elixhauser categories, a well-established scheme distinguishing 30 classes of chronic diseases. We used latent class analysis to identify distinct patient subgroups based on demographics, admission type, and morbidity compositions and compared the prevalence of organ dysfunction, sepsis, and inpatient mortality for each subgroup. Results We identified six clinically distinct multimorbidity subgroups labeled based on their dominant Elixhauser disease classes. The “cardiopulmonary” and “cardiac” subgroups consisted of older patients with a high prevalence of cardiopulmonary conditions and constituted 6.1% and 26.4% of study cohort respectively. The “young” subgroup included 23.5% of the cohort composed of young and healthy patients. The “hepatic/addiction” subgroup, constituting 9.8% of the cohort, consisted of middle-aged patients (mean age of 52.25, 95% CI 51.85–52.65) with the high rates of depression (20.1%), alcohol abuse (47.75%), drug abuse (18.2%), and liver failure (67%). The “complicated diabetics” and “uncomplicated diabetics” subgroups constituted 9.4% and 24.8% of the study cohort respectively. The complicated diabetics subgroup demonstrated higher rates of end-organ complications (88.3% prevalence of renal failure). Rates of organ dysfunction and sepsis ranged 19.6–69% and 12.5–46.7% respectively in the six subgroups. Mortality co-occurring with organ dysfunction and sepsis ranges was 8.4–23.8% and 11.7–27.4% respectively. These adverse outcomes were most prevalent in the hepatic/addiction subgroup. Conclusion We identify distinct multimorbidity states that associate with relatively higher prevalence of organ dysfunction, sepsis, and co-occurring mortality. The findings promote the incorporation of multimorbidity in healthcare models and the shift away from the current single-disease paradigm in clinical practice, training, and trial design. Electronic supplementary material The online version of this article (10.1186/s13054-019-2486-6) contains supplementary material, which is available to authorized users.

Subphenotypes in critical care: translation into clinical practice

“…22 Identified groups were: the "cardiopulmonary" (6•1% prevalence) and "cardiac" subphenotypes (26•4% prevalence), consisting of older patients with cardiopulmonary conditions; the "young" subphenotype (23•5% prevalence) consisting of young, healthy patients; the "hepatic/addiction" subphenotype (9•8% prevalence) consisting of middle-aged patients with high rates of depression, substance abuse, and liver failure; the "complicated diabetics" subphenotype (9•4% prevalence); and the "uncomplicated diabetics" subphenotype (24•8% prevalence). 22 The highest mortality groups were the "hepatic/addiction" subphenotype, followed by the "cardiac" subphenotype, then the "cardiopulmonary" and "complicated diabetics" subphenotypes. This study is the first to apply LCA to multi-morbidity and provides robust evidence for differing clinical outcomes based on multi-morbidity cluster.…”

“…In recent years, the rise of genomics, transcriptomics, proteomics, and metabolomics coupled with growth in data analytic tools has seen an exponential growth in the identification of novel disease subgroups (subphenotypes) that has led to numerous clinical and biological insights into acute respiratory distress syndrome (ARDS), [5][6][7][8][9][10][11][12][13][14][15][16][17][18] sepsis, [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] and acute kidney injury (AKI). 38,39 The advent of these subphenotypes offers the tantalizing prospect of delivering precision-based critical care medicine, as evidenced by data in other fields, such as oncology 40,41 and asthma, [42][43][44] where similar approaches have been successfully applied.…”

Pride and prejudice – What can we learn from peer review?

“… 2017 ; Zador et al. 2019 ). With this analysis, we aimed to identify possible unobserved groups of reviewers based on the following information: level of detail, length of review, number of comments, request for additional analysis, positiveness, harshness, whether the reviewer requested the second round of reviews, and the days between the submission date and the date the review was received by the editor.…”

“…To identify polytomous classes of reviewers, we applied latent class analysis. This is an unsupervised statistical method for identifying unmeasured class membership among subjects using observed categorical and/or continuous variables (Formann 1992;Linzer and Lewis 2011), used in many studies to stratify disease populations (Keel et al 2004;Kim et al 2016;Molgaard Nielsen et al 2017;Zador et al 2019). With this analysis, we aimed to identify possible unobserved groups of reviewers based on the following information: level of detail, length of review, number of comments, request for additional analysis, positiveness, harshness, whether the reviewer requested the second round of reviews, and the days between the submission date and the date the review was received by the editor.…”

Attitudes of multimorbid patients to surviving future acute illness and subsequent functional disability: A systematic review

“…These include in-hospital and post-hospital rates of death, as well as greater morbidity and dependence resulting in the use of care facilities instead of being cared for at home. 6 7 8 Attitudes to this acquired disability are ill defined regarding the consequences of acute hospitalisation - specifically in the context of survival with additional illness and acquired functional disability.…”