Creation and Expansion of a Mixed Patient Intermediate Care Unit to Improve ICU Capacity.

OBJECTIVES: ICU capacity strain is associated with worsened outcomes. Intermediate care units (IMCs) comprise one potential option to offload ICUs while providing appropriate care for intermediate acuity patients, but their impact on ICU capacity has not been thoroughly characterized. The aims of this study are to describe the creation of a medical-surgical IMC and assess how the IMC affected ICU capacity. DESIGN: Descriptive report with retrospective cohort review. SETTING: Six hundred seventy-three-bed tertiary care academic medical center with 77 ICU beds. PATIENTS: Adult inpatients who were admitted to the IMC. INTERVENTIONS: An interdisciplinary working group created an IMC which was located on a general ward. The IMC was staffed by hospitalists and surgeons and supported by critical care consultants. The initial maximum census was three, but this number increased to six in response to heightened critical care demand. IMC admission criteria also expanded to include advanced noninvasive respiratory support defined as patients requiring high-flow nasal cannula, noninvasive positive pressure ventilation, or mechanical ventilation in patients with tracheostomies. MEASUREMENTS AND MAIN RESULTS: The primary outcome entailed the number of ICU bed-days saved. Adverse outcomes, including ICU transfer, intubation, and death, were also recorded. From August 2021 to July 2022, 230 patients were admitted to the IMC. The most frequent IMC indications were respiratory support for medical patients and post-operative care for surgical patients. A total of 1023 ICU bed-days were made available. Most patients were discharged from the IMC to a general ward, while 8% of all patients required transfer to an ICU within 48 hours of admission. Intubation (2%) and death (1%) occurred infrequently within 48 hours of admission. Respiratory support was the indication associated with the most ICU transfers. CONCLUSIONS: Despite a modest daily census, an IMC generated substantial ICU bed capacity during a time of peak critical care demand.

Evaluation of evolving sepsis screening criteria in discriminating suspected sepsis and mortality among adult patients admitted to the intensive care unit.

BACKGROUND: Institutions struggle with successful use of sepsis alerts within electronic health records. OBJECTIVE: Test the association of sepsis screening measurement criteria in discrimination of mortality and detection of sepsis in a large dataset. DESIGN: Retrospective, cohort study using a large United States (U.S.) intensive care database. The Institutional Review Board exempt status was obtained from Kansas University Medical Center Human Research Protection Program (10-1-2015). SETTING: 334 U.S. hospitals participating in the eICU Research Institute. PARTICIPANTS: Nine hundred twelve thousand five hundred and nine adult intensive care admissions from 183 hospitals. METHODS: Exposures included: systemic inflammatory response syndrome criteria ≥ 2 (Sepsis-1); systemic inflammatory response syndrome criteria with organ failure criteria ≥ 3.5 points (Sepsis-2); and sepsis-related organ failure assessment score ≥ 2 and quick score ≥ 2 (Sepsis-3). Discrimination of outcomes was determined with/without (adjusted/unadjusted) baseline risk exposure to a model. The receiver operating characteristic curve (AUROC) and odds ratios (ORs) for each decile of baseline risk of sepsis or death were assessed. RESULTS: Within the eligible cohort of 912,509, a total of 86,219 (9.4 %) patients did not survive their hospital stay and 186,870 (20.5 %) met the definition of suspected sepsis. For suspected sepsis discrimination, Sepsis-2 (unadjusted AUROC 0.67, 99 % CI: 0.66-0.67 and adjusted AUROC 0.77, 99 % CI: 0.77-0.77) outperformed Sepsis-3 (SOFA unadjusted AUROC 0.61, 99 % CI: 0.61-0.61 and adjusted AUROC 0.74, 99 % CI: 0.74-0.74) (qSOFA unadjusted AUROC 0.59, 99 % CI: 0.59-0.60 and adjusted AUROC 0.73, 99 % CI: 0.73-0.73). Sepsis-2 also outperformed Sepsis-1 (unadjusted AUROC 0.58, 99 % CI: 0.58-0.58 and adjusted AUROC 0.73, 99 % CI: 0.73-0.73). In between differences of AUROCs were statistically significantly different. Sepsis-2 ORs were higher for the outcome of suspected sepsis when considering deciles of risk than the other measurement systems. CONCLUSIONS AND RELEVANCE: Sepsis-2 outperformed other systems in suspected sepsis detection and was comparable to SOFA in prognostic accuracy of mortality in adult intensive care patients.

The Global Open Source Severity of Illness Score (GOSSIS).

OBJECTIVES: To develop and demonstrate the feasibility of a Global Open Source Severity of Illness Score (GOSSIS)-1 for critical care patients, which generalizes across healthcare systems and countries. DESIGN: A merger of several critical care multicenter cohorts derived from registry and electronic health record data. Data were split into training (70%) and test (30%) sets, using each set exclusively for development and evaluation, respectively. Missing data were imputed when not available. SETTING/PATIENTS: Two large multicenter datasets from Australia and New Zealand (Australian and New Zealand Intensive Care Society Adult Patient Database [ANZICS-APD]) and the United States (eICU Collaborative Research Database [eICU-CRD]) representing 249,229 and 131,051 patients, respectively. ANZICS-APD and eICU-CRD contributed data from 162 and 204 hospitals, respectively. The cohort included all ICU admissions discharged in 2014-2015, excluding patients less than 16 years old, admissions less than 6 hours, and those with a previous ICU stay. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: GOSSIS-1 uses data collected during the ICU stay's first 24 hours, including extrema values for vital signs and laboratory results, admission diagnosis, the Glasgow Coma Scale, chronic comorbidities, and admission/demographic variables. The datasets showed significant variation in admission-related variables, case-mix, and average physiologic state. Despite this heterogeneity, test set discrimination of GOSSIS-1 was high (area under the receiver operator characteristic curve [AUROC], 0.918; 95% CI, 0.915-0.921) and calibration was excellent (standardized mortality ratio [SMR], 0.986; 95% CI, 0.966-1.005; Brier score, 0.050). Performance was held within ANZICS-APD (AUROC, 0.925; SMR, 0.982; Brier score, 0.047) and eICU-CRD (AUROC, 0.904; SMR, 0.992; Brier score, 0.055). Compared with GOSSIS-1, Acute Physiology and Chronic Health Evaluation (APACHE)-IIIj (ANZICS-APD) and APACHE-IVa (eICU-CRD), had worse discrimination with AUROCs of 0.904 and 0.869, and poorer calibration with SMRs of 0.594 and 0.770, and Brier scores of 0.059 and 0.063, respectively. CONCLUSIONS: GOSSIS-1 is a modern, free, open-source inhospital mortality prediction algorithm for critical care patients, achieving excellent discrimination and calibration across three countries.

Data-driven curation process for describing the blood glucose management in the intensive care unit.

Analysis of real-world glucose and insulin clinical data recorded in electronic medical records can provide insights into tailored approaches to clinical care, yet presents many analytic challenges. This work makes publicly available a dataset that contains the curated entries of blood glucose readings and administered insulin on a per-patient basis during ICU admissions in the Medical Information Mart for Intensive Care (MIMIC-III) database version 1.4. Also, the present study details the data curation process used to extract and match glucose values to insulin therapy. The curation process includes the creation of glucose-insulin pairing rules according to clinical expert-defined physiologic and pharmacologic parameters. Through this approach, it was possible to align nearly 76% of insulin events to a preceding blood glucose reading for nearly 9,600 critically ill patients. This work has the potential to reveal trends in real-world practice for the management of blood glucose. This data extraction and processing serve as a framework for future studies of glucose and insulin in the intensive care unit.

Vasopressin Administration Is Associated With Rising Serum Lactate Levels in Patients With Sepsis.

BACKGROUND: Vasopressin is used in conjunction with norepinephrine during treatment of patients with septic shock. Serum lactate is often used in monitoring of patients with sepsis; however, its importance as a therapeutic target is unclear. The objective of this study is to examine the relationship of vasopressin use on serum lactate levels in patients with sepsis. METHODS: This study uses electronic heath records available via the Medical Information Mart for Intensive Care III. Patients were required to have a serum lactate monitoring during the intensive care unit (ICU) stay. The treatment was the administration of vasopressin between hours 3 and 18 of the ICU stay. Analysis was performed using a matched design. RESULTS: Patients receiving vasopressin were more likely to have their serum lactate levels rise when compared to matched patients who did not receive vasopressin (odds ratio: 6.6; 95% confidence interval: 3.0-14.6, P < .001). Patients who received vasopressin had a median increase in serum lactate of 0.3 mmol/L, while patients who did not receive vasopressin had a median decrease in serum lactate of 0.7 mmol/L (P < .001). There was no statistically significant difference between the control and treated groups' lactate trajectories prior to possible administration of vasopressin (P = .15). The results did not change significantly when norepinephrine initiation was used as the index time. CONCLUSIONS: In patients with sepsis, the administration of vasopressin was associated with a statistically significant difference in lactate change over the course of 24 hours when compared to matched patients who did not receive vasopressin.

Impact of Intensive Care Unit Discharge Delays on Patient Outcomes: A Retrospective Cohort Study.

OBJECTIVE: Patients often overstay in intensive care units (ICU) after they are deemed discharge ready. The objective of this study was to examine the impact of such discharge delays (DD) on subsequent in-hospital morbidity and mortality. DESIGN: Retrospective cohort study. SETTING: Single tertiary academic medical center. PATIENTS: Adult patients admitted to the medical ICU between 2005 and 2011. INTERVENTIONS: For all patients, DD (ie, time between request for a ward bed and time of ICU discharge) was calculated. Discharge delays was dichotomized as long (≥24 hours) or short (<24 hours). Multivariable linear and logistic regressions were used to assess the association between dichotomized DD and post-ICU clinical outcomes. RESULTS: Overall, 9673 discharges were included of which 10.4% patients had long DDs. In the fully adjusted model, a long delay was not associated with increased odds of death (adjusted odds ratio [aOR]: 0.99, 95% confidence interval [CI]: 0.74-1.31, P = .95) but was associated with a shorter log plus one of post-ICU discharge length of stay (LOS; regression coefficient: -0.13, 95% CI: -0.17 to -0.08, P < .001). Longer DD was not associated with more hospital-free days (HFD: a composite of post-ICU LOS and in-hospital mortality). Shorter DDs were associated with shorter LOS when LOS was measured from the time of ward bed request as opposed to the time of ICU discharge. CONCLUSIONS: In this study, long DD was associated with a slight decrease in post-ICU LOS but longer LOS when measured from the point of ward bed request, suggesting a potential role for more aggressive discharge planning in the ICU for patients with long DDs. There was no association between long DD and subsequent mortality or HFD.

Big data and machine learning in critical care: Opportunities for collaborative research.

The introduction of clinical information systems (CIS) in Intensive Care Units (ICUs) offers the possibility of storing a huge amount of machine-ready clinical data that can be used to improve patient outcomes and the allocation of resources, as well as suggest topics for randomized clinical trials. Clinicians, however, usually lack the necessary training for the analysis of large databases. In addition, there are issues referred to patient privacy and consent, and data quality. Multidisciplinary collaboration among clinicians, data engineers, machine-learning experts, statisticians, epidemiologists and other information scientists may overcome these problems. A multidisciplinary event (Critical Care Datathon) was held in Madrid (Spain) from 1 to 3 December 2017. Under the auspices of the Spanish Critical Care Society (SEMICYUC), the event was organized by the Massachusetts Institute of Technology (MIT) Critical Data Group (Cambridge, MA, USA), the Innovation Unit and Critical Care Department of San Carlos Clinic Hospital, and the Life Supporting Technologies group of Madrid Polytechnic University. After presentations referred to big data in the critical care environment, clinicians, data scientists and other health data science enthusiasts and lawyers worked in collaboration using an anonymized database (MIMIC III). Eight groups were formed to answer different clinical research questions elaborated prior to the meeting. The event produced analyses for the questions posed and outlined several future clinical research opportunities. Foundations were laid to enable future use of ICU databases in Spain, and a timeline was established for future meetings, as an example of how big data analysis tools have tremendous potential in our field.

How is the Doctor Feeling? ICU Provider Sentiment is Associated with Diagnostic Imaging Utilization.

The judgment of intensive care unit (ICU) providers is difficult to measure using conventional structured electronic medical record (EMR) data. However, provider sentiment may be a proxy for such judgment. Utilizing 10 years of EMR data, this study evaluates the association between provider sentiment and diagnostic imaging utilization. We extracted daily positive / negative sentiment scores of written provider notes, and used a Poisson regression to estimate sentiment association with the total number of daily imaging reports. After adjusting for confounding factors, we found that (1) negative sentiment was associated with increased imaging utilization $(p < 0.01)$, (2) sentiment's association was most pronounced at the beginning of the ICU stay $(p < 0.01)$, and (3) the presence of any form of sentiment increased diagnostic imaging utilization up to a critical threshold $(p < 0.01)$. Our results indicate that provider sentiment may clarify currently unexplained variance in resource utilization and clinical practice.

The eICU Collaborative Research Database, a freely available multi-center database for critical care research.

Critical care patients are monitored closely through the course of their illness. As a result of this monitoring, large amounts of data are routinely collected for these patients. Philips Healthcare has developed a telehealth system, the eICU Program, which leverages these data to support management of critically ill patients. Here we describe the eICU Collaborative Research Database, a multi-center intensive care unit (ICU)database with high granularity data for over 200,000 admissions to ICUs monitored by eICU Programs across the United States. The database is deidentified, and includes vital sign measurements, care plan documentation, severity of illness measures, diagnosis information, treatment information, and more. Data are publicly available after registration, including completion of a training course in research with human subjects and signing of a data use agreement mandating responsible handling of the data and adhering to the principle of collaborative research. The freely available nature of the data will support a number of applications including the development of machine learning algorithms, decision support tools, and clinical research.

A Comparative Analysis of Sepsis Identification Methods in an Electronic Database.

OBJECTIVES: To evaluate the relative validity of criteria for the identification of sepsis in an ICU database. DESIGN: Retrospective cohort study of adult ICU admissions from 2008 to 2012. SETTING: Tertiary teaching hospital in Boston, MA. PATIENTS: Initial admission of all adult patients to noncardiac surgical ICUs. INTERVENTIONS: Comparison of five different algorithms for retrospectively identifying sepsis, including the Sepsis-3 criteria. MEASUREMENTS AND MAIN RESULTS: 11,791 of 23,620 ICU admissions (49.9%) met criteria for the study. Within this subgroup, 59.9% were suspected of infection on ICU admission, 75.2% of admissions had Sequential Organ Failure Assessment greater than or equal to 2, and 49.1% had both suspicion of infection and Sequential Organ Failure Assessment greater than or equal to 2 thereby meeting the Sepsis-3 criteria. The area under the receiver operator characteristic of Sequential Organ Failure Assessment (0.74) for hospital mortality was consistent with previous studies of the Sepsis-3 criteria. The Centers for Disease Control and Prevention, Angus, Martin, Centers for Medicare & Medicaid Services, and explicit coding methods for identifying sepsis revealed respective sepsis incidences of 31.9%, 28.6%, 14.7%, 11.0%, and 9.0%. In-hospital mortality increased with decreasing cohort size, ranging from 30.1% (explicit codes) to 14.5% (Sepsis-3 criteria). Agreement among the criteria was acceptable (Cronbach's alpha, 0.40-0.62). CONCLUSIONS: The new organ dysfunction-based Sepsis-3 criteria have been proposed as a clinical method for identifying sepsis. These criteria identified a larger, less severely ill cohort than that identified by previously used administrative definitions. The Sepsis-3 criteria have several advantages over prior methods, including less susceptibility to coding practices changes, provision of temporal context, and possession of high construct validity. However, the Sepsis-3 criteria also present new challenges, especially when calculated retrospectively. Future studies on sepsis should recognize the differences in outcome incidence among identification methods and contextualize their findings according to the different cohorts identified.

tableone: An open source Python package for producing summary statistics for research papers.

OBJECTIVES: In quantitative research, understanding basic parameters of the study population is key for interpretation of the results. As a result, it is typical for the first table ("Table 1") of a research paper to include summary statistics for the study data. Our objectives are 2-fold. First, we seek to provide a simple, reproducible method for providing summary statistics for research papers in the Python programming language. Second, we seek to use the package to improve the quality of summary statistics reported in research papers. MATERIALS AND METHODS: The tableone package is developed following good practice guidelines for scientific computing and all code is made available under a permissive MIT License. A testing framework runs on a continuous integration server, helping to maintain code stability. Issues are tracked openly and public contributions are encouraged. RESULTS: The tableone software package automatically compiles summary statistics into publishable formats such as CSV, HTML, and LaTeX. An executable Jupyter Notebook demonstrates application of the package to a subset of data from the MIMIC-III database. Tests such as Tukey's rule for outlier detection and Hartigan's Dip Test for modality are computed to highlight potential issues in summarizing the data. DISCUSSION AND CONCLUSION: We present open source software for researchers to facilitate carrying out reproducible studies in Python, an increasingly popular language in scientific research. The toolkit is intended to mature over time with community feedback and input. Development of a common tool for summarizing data may help to promote good practice when used as a supplement to existing guidelines and recommendations. We encourage use of tableone alongside other methods of descriptive statistics and, in particular, visualization to ensure appropriate data handling. We also suggest seeking guidance from a statistician when using tableone for a research study, especially prior to submitting the study for publication.

Severity of Illness Scores May Misclassify Critically Ill Obese Patients.

OBJECTIVE: Severity of illness scores rest on the assumption that patients have normal physiologic values at baseline and that patients with similar severity of illness scores have the same degree of deviation from their usual state. Prior studies have reported differences in baseline physiology, including laboratory markers, between obese and normal weight individuals, but these differences have not been analyzed in the ICU. We compared deviation from baseline of pertinent ICU laboratory test results between obese and normal weight patients, adjusted for the severity of illness. DESIGN: Retrospective cohort study in a large ICU database. SETTING: Tertiary teaching hospital. PATIENTS: Obese and normal weight patients who had laboratory results documented between 3 days and 1 year prior to hospital admission. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Seven hundred sixty-nine normal weight patients were compared with 1,258 obese patients. After adjusting for the severity of illness score, age, comorbidity index, baseline laboratory result, and ICU type, the following deviations were found to be statistically significant: WBC 0.80 (95% CI, 0.27-1.33) × 10/L; p = 0.003; log (blood urea nitrogen) 0.01 (95% CI, 0.00-0.02); p = 0.014; log (creatinine) 0.03 (95% CI, 0.02-0.05), p < 0.001; with all deviations higher in obese patients. A logistic regression analysis suggested that after adjusting for age and severity of illness at least one of these deviations had a statistically significant effect on hospital mortality (p = 0.009). CONCLUSIONS: Among patients with the same severity of illness score, we detected clinically small but significant deviations in WBC, creatinine, and blood urea nitrogen from baseline in obese compared with normal weight patients. These small deviations are likely to be increasingly important as bigger data are analyzed in increasingly precise ways. Recognition of the extent to which all critically ill patients may deviate from their own baseline may improve the objectivity, precision, and generalizability of ICU mortality prediction and severity adjustment models.

Bridging the Health Data Divide.

Fundamental quality, safety, and cost problems have not been resolved by the increasing digitization of health care. This digitization has progressed alongside the presence of a persistent divide between clinicians, the domain experts, and the technical experts, such as data scientists. The disconnect between clinicians and data scientists translates into a waste of research and health care resources, slow uptake of innovations, and poorer outcomes than are desirable and achievable. The divide can be narrowed by creating a culture of collaboration between these two disciplines, exemplified by events such as datathons. However, in order to more fully and meaningfully bridge the divide, the infrastructure of medical education, publication, and funding processes must evolve to support and enhance a learning health care system.

Estimating relationships between phenotypes and subjects drawn from admixed families.

BACKGROUND: Estimating relationships among subjects in a sample, within family structures or caused by population substructure, is complicated in admixed populations. Inaccurate allele frequencies can bias both kinship estimates and tests for association between subjects and a phenotype. We analyzed the simulated and real family data from Genetic Analysis Workshop 19, and were aware of the simulation model. RESULTS: We found that kinship estimation is more accurate when marker data include common variants whose frequencies are less variable across populations. Estimates of heritability and association vary with age for longitudinally measured traits. Accounting for local ancestry identified different true associations than those identified by a traditional approach. Principal components aid kinship estimation and tests for association, but their utility is influenced by the frequency of the markers used to generate them. CONCLUSIONS: Admixed families can provide a powerful resource for detecting disease loci, as well as analytical challenges. Allele frequencies, although difficult to adequately estimate in admixed populations, have a strong impact on the estimation of kinship, ancestry, and association with phenotypes. Approaches that acknowledge population structure in admixed families outperform those which ignore it.

Power and Effective Study Size in Heritability Studies.

Correlation between study units in quantitative genetics studies often makes it difficult to compare important inferential aspects of studies. Describing the relatedness between study units is critical to capture features of pedigree studies involving heritability, including power and precision of heritability estimates. Blangero et al (2012) showed that in pedigree studies the power to detect heritability is a function of the true heritability and the eigenvalues of the kinship matrix. We extend this to a more general setting which allows statements about expected precision of heritability estimates. Using two different Taylor series approximations, we summarize the relatedness in a study design by one or two parameters. These relatedness summary parameters (RSPs) are functions of the eigenvalues or log-eigenvalues of the kinship matrix. Using the RSPs based on the log-eigenvalues, we accurately approximate the expectation of the likelihood ratio test and expected confidence interval widths. We define an effective sample size of a target study as one which has the equivalent power and precision to a reference design. Using unrelated sibpairs as the reference design provides very accurate assessments of power. RSPs and effective sample sizes provide new tools for comparing studies and communicating information about relatedness in heritability studies.

Multivariate longitudinal data analysis with mixed effects hidden Markov models.

Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies.

Continued low uptake of treatment for hepatitis C virus infection in a large community-based cohort of inner city residents.

BACKGROUND & AIMS: Despite advances in HCV treatment, recent data on treatment uptake is sparse. HCV treatment uptake and associated factors were evaluated in a community-based cohort in Vancouver, Canada. METHODS: The CHASE study is a cohort of inner city residents recruited from January 2003-June 2004. HCV status and treatment were retrospectively and prospectively determined through data linkages with provincial virology and pharmacy databases. Logistic regression analyses were used to identify factors associated with HCV treatment uptake. RESULTS: Among 2913, HCV antibody testing was performed in 2405, 64% were HCV antibody-positive (n = 1533). Individuals with spontaneous clearance (18%, n = 276) were excluded. Among the remaining 1257 HCV antibody-positive participants (mean age 42, 71% male), 29% were Aboriginal. At enrolment, the majority reported recent injecting (60%) and non-injecting drug use (87%). Between January 1998 and March 2010, 6% (77 of 1257) initiated HCV treatment. In adjusted analyses, Aboriginal ethnicity [adjusted odds ratio (AOR) 0.23; 95% CI 0.10, 0.51] and crack cocaine use (AOR 0.61; 95% CI 0.37, 0.99) were associated with a decreased odds of receiving HCV treatment, while methamphetamine injecting (AOR 0.16; 95% CI 0.02, 1.18) trended towards a lower odds of receiving treatment. HCV treatment uptake ranged from 0.2 (95% CI 0.0, 0.7) per 100 person-years (PYs) in 2003 to 1.6 (95% CI 0.9, 2.6) per 100 PYs in 2009. CONCLUSION: HCV treatment uptake remains low in this large community-based cohort of inner city residents with a high HCV prevalence and access to universal healthcare.

Mortality in a large community-based cohort of inner-city residents in Vancouver, Canada.

BACKGROUND: The Downtown Eastside is a robust and densely populated neighbourhood in Vancouver, Canada, that is characterized by low-income housing and drug use and a high prevalence of HIV infection. We evaluated mortality and excess mortality among the broader community of individuals living in this neighbourhood. METHODS: The Community Health and Safety Evaluation is a community-based study of inner-city residents in the Downtown Eastside who were recruited in 2003 and 2004. Participants' data were linked with data in provincial virology and mortality databases retrospectively and prospectively for the period 1991-2009. Mortality and standardized mortality ratios (SMRs) were calculated for the period 2003-2009 to compare death rates in the study population with rates in the population of Vancouver. RESULTS: Among 2913 participants, 374 deaths occurred, for an all-cause mortality of 223 per 10 000 person-years (95% confidence interval [CI] 201-247 per 10 000 person-years). Compared with the population of Vancouver, significant excess mortality was observed in the study population (SMR 7.1, 95% CI 6.4-7.9). Excess mortality was higher among women (SMR 15.4, 95% CI 12.8-18.5) than among men (SMR 5.8, 95% CI 5.1-6.6). Although crude mortality increased with age, excess mortality was greatest among participants less than 35 years old (SMR 13.2, 95% CI 9.4-18.5) and those 35-39 years old (SMR 13.3, 95% CI 10.3-17.1). Excess risk was also elevated among participants with hepatitis C virus (HCV), HIV and HCV/HIV infection, with SMRs of 5.9 (95% CI 4.9-7.1), 19.2 (95% CI 12.8-28.9) and 23.0 (95% CI 19.3-27.4), respectively. INTERPRETATION: Our study showed high mortality in this inner-city population, particularly when compared with the general population of Vancouver. Excess mortality was highest among women, younger participants and those infected with either HCV or HIV or both.