The field of critical care medicine is rapidly expanding in the United States.1–5 Over a period of two decades, the total number of intensive care unit (ICU) beds has increased by more than 35% (from 69,300 beds in 1985 to 93,955 in 2005) along with a 44% increase in ICU length of stay (LOS) (16.1 million inpatients days to 23.2 million inpatient days).2,3 Mirroring these trends, the costs of providing critical care have also escalated disproportionately. From 2000 to 2005, there was a 44.2% rise in spending, with a total of US $81.7 billion accounting of 0.66% of US gross domestic product spent in 2015.3 The compounding effects of an aging population, increased insurance coverage, limited workforce, rising complexity of care, and increasing severity of illness will exponentially increase the need for critical care services in the future.6
Despite the burden of disease and rising critical care exigency in the United States, the epidemiology of critical illness remains inadequately characterized, especially within the surgical literature.6 Several authors attribute this to either the lack of a national, centralized, administrative, or outcomes-based data repository that accurately captures critical care admissions or the inability of procedure or hospital-specific diagnosis codes to effectively identify the entire spectrum of critical illness.6,7
Several studies report temporal decreases in hospital mortality and morbidity for various disease processes that commonly require ICU level care, such as sepsis, coronary artery bypass graftings, and other high-risk procedures.8–11 Large ICU-based cohort studies from Australasia, England, and Canada have demonstrated significant temporal reductions in risk-adjusted mortality and ICU LOS, and recent attempts from the United States also note similar trends.12–15 However, these studies largely focus on medical ICUs and nonsurgical admissions; to our knowledge, no study has exclusively evaluated surgical ICU admissions within the United States. Such an appraisal helps characterize the variety of surgical patients admitted to ICUs and can increase the efficiency of health care delivery by identifying needs for resource and personnel allocation. Furthermore, these efforts help identify priorities for training, education, and outcomes-based research.14,15
In our current study, we leverage a national ICU outcomes database from the United States and model the temporal variation of surgical admissions within ICUs, mortality, ICU LOS, and the change of functional status (FS) over almost a decade. We hypothesize that the population of surgical patients admitted to ICUs has increased over the last decade, with decreases in ICU mortality and ICU LOS, along with improvements to FS from baseline.
PATIENTS AND METHODS
Data and Study Design
We performed a 9-year retrospective, temporal, cohort analysis using the Acute Physiology and Chronic Health Evaluation (APACHE) outcomes database (Cerner Corporation, Kansas City, MO). This database includes prospectively collected clinical data from a cohort of 238 nonrandomized ICUs across the United States from 2008 to 2016. The APACHE outcomes database compiles deidentified data from all participating ICUs that voluntarily subscribe to its national benchmarking and quality improvement service.16 It collects clinical, physiological, and laboratory-based data including primary diagnosis for ICU admission, patient location before admission, severity scores, ventilator status, and duration of ICU LOS during the first 24 hours following admission, along with outcomes-based data from all patients admitted to the ICU.16–18 The APACHE outcomes use a combination of trained clinical coordinators and an integrated electronic interface to abstract data.19 Each coordinator is enrolled in a standardized, three-phase training session, and an automated software ensures that the data collected from the electronic interface are valid and devoid of incongruous or erroneous data points.20 Details of data collection, validation, reliability, and data audit have been described elsewhere.18–24 Our institutional ethical review board approved this study (UMN STUDY00001489).
Inclusion and Exclusion Criteria
The APACHE outcomes database included a total of 345,650 adult ICU admissions from 2008 to 2016, from 238 ICUs. From this cohort, we included all individual patient encounters that had a primary surgical diagnosis on ICU admission from 2008 to 2016. We then excluded all patients who were younger than 15 years. We also excluded any ICU with <3 years of continuous, temporal data, or any ICU with <25 patient admissions within any given year, during the study period (Fig. 1).
To facilitate temporal modeling, we classified all patients into 13 distinct surgical cohorts. The classification was based on the type of surgical procedure, the primary surgical team providing care, and the involved organ system. The surgical cohorts included the following: transplant; orthopedic; breast, soft-tissue, and extremity; vascular; hepatobiliary; cardiac; thoracic; gastrointestinal; urological; obstetric and gynecological; trauma; neurosurgery and spine; and facial and ear, nose, and throat (ENT) surgery. Details of each surgical procedure and the diagnoses included under each cohort are provided in Supplemental Digital Content 1 (Table 1, http://links.lww.com/TA/B677).
Outcomes of Interest
Our primary outcome of interest was the temporal change in the incidence of surgical admissions within ICUs. Our secondary outcomes included the change in mortality, LOS, and the rate of change in FS from baseline within each surgical cohort. Functional status was captured as an ordinal variable, both at ICU admission and at hospital discharge, and was assigned to one of three categories (independent, partially dependent, and fully dependent). A designation of declined or remained stationary FS was based on the change, or lack thereof, in FS from admission to hospital discharge.
We used generalized mixed-effects regression to account for the clustered nature of the data and to adjust for potential hospital-specific variation. Mortality and ICU admission incidence rates were modeled at the hospital level using generalized negative-binomial models. Year was the predictor of interest, with a random intercept per ICU and an offset for the log number of cases per ICU per year in each epidemiologic category. Mortality models were additionally adjusted for severity of disease presentation using the validated APACHE III score.23 All potential confounders were centered and scaled before fitting. We modeled ICU LOS using patient-level, generalized, linear mixed-effects models with a random intercept for hospital, the year term as the predictor of interest, and adjusted for unscaled age, race, sex and APACHE III score. To model the rate of changing FS, we used hospital-level Quasi-Poisson models with an offset of log cases per hospital, per year, and per epidemiologic classification and year as the predictor of interest. For each FS model, we adjusted for age, race, sex, and APACHE III score using centered and scaled covariates. Despite fitting a large number of models, we did not correct for multiple hypothesis testing because this study was primarily exploratory in nature. Incidence rate ratios (IRRs) for ICU admissions, mortality, and FS, along with estimates for LOS are reported and graphed with 95% confidence intervals for a 1-year increase. For the FS models, the estimate denotes the IRR of declining or stationary FS during an ICU visit for a 1-year increase in time.
Missing Variable Analysis
Missing variable analysis identified four variables with missing data: race (n = 1,179, 1.5%), APACHE III score (n = 104, 0.1%), hospital LOS (n = 6, <0.01%), and discharge FS (n = 19,290, n = 24.7%). Little's test determined that the data were not missing completely at random (χ2 = 64.4, p < 0.001). We did not use multiple imputation methods to account for missing data and instead chose complete case analysis for model creation because (1) three of the four variables with missing data had a very low percentages of data missing; (2) FS, which had the highest proportion of missing data, was an outcome/dependent variable; multiple imputation techniques are not recommended in these situations as they can inflate standard errors and introducing uncertainty into the models; and (3) pattern analysis of the missing data determined that data missing for FS were unreported for all patients from certain participating hospitals; it is highly unlikely that this pattern of missing data would systemically bias our models.
We performed a post hoc, best- and worst-case sensitivity analysis for the overall change in discharge FS, which had nearly 25% missing data. At the person level, we recoded those patients with a missing change in FS as “stationary” or “independent to fully dependent” for best- and worst-case scenarios, respectively. We then modeled the rate of changing FS using hospital-level Quasi-Poisson models with the same covariates described earlier.
All analyses were performed using R (version 3.4.0, R Core Team 2019, R: A language and environment for statistical computing; R Foundation for Statistical Computing, Vienna, Austria; https://www.R-project.org) and STATA (Stata Statistical Software: Release 15. StataCorp LLC, 2017, College Station, TX).
Of the 345,650 adult ICU admissions, 97,893 (28.3% of all ICU admissions) had a primary surgical diagnosis for ICU admission. A total of 78,053 patient admissions, from 88 ICUs across the United States, met our inclusion criteria (Fig. 1). Intensive care unit and patient characteristics are shown in Table 1 and Table 2, respectively. On average, each participating hospital received 343 ± 204 ICU admissions annually. Geographically, the APACHE database captured data from all four major regions of the United States. However, in comparison with national metrics reported by the Centers for Medicare and Medicaid Services (CMS) Hospital Cost Report in the 2011, ICUs from the South and Midwest regions were overrepresented in our current analysis (proportion of ICUs from national sample: CMS data — South, 16.3%; Midwest, 22.6% vs. APACHE data — South, 40.9%; Midwest, 34.1%).5
Similarly, our data sampled a higher proportion of teaching hospitals than CMS data (proportion of ICUs from national CMS sample — large teaching, 28.5%; small teaching, 31.5% vs. APACHE data — large teaching, 38.6%; small teaching, 30.7%).5 A higher percentage of patients were male (56.8%) with a mean ± SD age of 62.8 ± 15.9 years. While all racial groups were sampled, in comparison with the 2010 United States Census, a higher proportion of patients were White (72.4% vs. 83.9%) with a mean ± SD APACHE III score of 47.8 ± 21.8.25 We observed that nearly 60% of patients were admitted to the ICU directly from home, while approximately 10% were referred from an outpatient clinic, and 7% transferred from the emergency department. Cardiothoracic surgery services admitted the highest volume of patients (n = 18,705, 24%) followed by general surgery (n = 11,722, 15.0%). On admission, 40% (n = 31,242) of patients had an independent FS, with approximately 26% (n = 20,471) being partially dependent and 4.4% (3,439) being fully dependent on external care.
Change in ICU Admissions
The overall incidence of surgical admissions to the ICU remained unchanged during the study period (IRR, 0.9; p = 0.961, Fig. 2A and Supplemental Digital Content 2, Table 2, http://links.lww.com/TA/B678). Transplant (IRR, 0.9; p = 0.001) and thoracic surgery admissions (IRR, 0.9; p < 0.001) decreased, while facial and ENT surgery (IRR, 1.1; p = 0.003) along with breast, soft tissue infections, and extremity surgery (IRR, 1.1; p < 0.001) admissions increased in incidence.
Change in Hospital Mortality for Patients Admitted to the ICU
We observed no temporal change in the overall, risk-adjusted mortality over the 9-year study interval (Fig. 2B and Supplemental Digital Content 3, Table 3, http://links.lww.com/TA/B679). However, certain surgical cohorts such as cardiac surgery (IRR, 0.8; p = 0.001), neuro/spine surgery (IRR, 0.8; p = 0.001), orthopedic surgery (IRR, 0.7; p = 0.039), and breast, soft-tissue infection, and extremity surgery (IRR, 0.7; p = 0.016) demonstrated a significant decrease in ICU mortality. Mortality after urological surgery also decreased but failed to achieve statistical significance (IRR, 0.7; p = 0.056).
Change in ICU LOS
Our study showed a decrease in overall ICU LOS (estimate, −0.06; p < 0.001, Fig. 2C and Supplemental Digital Content 4, Table 4, http://links.lww.com/TA/B680) with significant reductions following gastrointestinal, urologic, transplant, cardiac, and neuro/spine surgery.
Change in FS
Overall, we observed an increase in the IRR for declining and stationary FS (Fig. 2D and Supplemental Digital Content 5, Tables 5 and 6, http://links.lww.com/TA/B681). Orthopedic surgery (IRR, 1.04; p = 0.035), hepatobiliary surgery (IRR, 1.1; p = 0.004), and cardiac surgery (IRR, 1.1, p < 0.001) demonstrated the greatest increase in declining FS. Worst-case sensitivity analysis showed similar increases in IRR for stationary (IRR, 1.023; p = <0.001) and declining FS (IRR, 1.094; p = <0.001). Similarly, sensitivity analysis for best-case scenarios demonstrated increases in IRR for stationary (IRR, 1.030; p = <0.001) and declining FS (IRR, 1.135; p = <0.001, Supplemental Digital Content 6, Table 7, http://links.lww.com/TA/B682).
Our study is an early attempt to epidemiologically model surgical admissions and outcomes from multiple ICUs within the United States using a nonadministrative ICU database that has been used previously by other investigators.23,24 We demonstrate that, after adjusting for center-specific variation, the overall cohort of surgical patients requiring critical care services has changed significantly from 2008 to 2016, with concomitant changes in mortality, ICU LOS, and FS.
Temporal Changes in ICU Admissions
An increasing amount of hospital resources are directed toward providing critical care services resulting in a disproportionate increase in the number of ICU beds nationwide.1–4,26 Despite this increase, postoperative ICU utilization rates have decreased over time; in 2005, the utilization rates were 68%, which decreased to approximately 66% in 2010.27,28 We anticipated the incidence of surgical admissions in ICUs to increase, but our results demonstrate that, apart from certain surgical cohorts, overall incidence remained static. While it may remain arduous to prove causality, the decrease in ICU utilization may partially explain our findings.
Other longitudinal ICU models have observed significant trends in medical admissions, where the frequency of certain disease processes such as sepsis, pneumonia, congestive heart failure, and diabetic ketoacidosis increased, while others such as asthma and gastrointestinal obstruction decreased in a temporal fashion.29 However, a similar evaluation among surgical admissions is lacking. In our study, rates of ICU admissions for facial and ENT surgery, and surgery for necrotizing soft tissue infections increased. Similarly, rates of transplant and thoracic surgery decreased. While it is beyond the scope of this article to review each trend in detail, our findings are congruent with recent epidemiological studies that document increases in the incidence of oropharyngeal cancers30 and necrotizing soft tissue infections31 and decreases in the incidence of lung, bronchus, and esophageal cancer for the past decade.32,33 Epidemiologically monitoring ICU trends may help project future resource and personnel requirements. Such evaluations help ICU administrators and providers better anticipate cohort-specific needs, providing ample time for these systems to evolve and adapt.
Temporal Changes in Mortality
Hospital mortality after ICU admission has decreased temporally; however, these rates vary significantly based on geography, diagnosis, case mix of the ICU, and period of review.12,13,15,29 An earlier review of US ICUs that spanned for 24 years (1988–2012) noted a 35% relative reduction in unadjusted mortality,15 while a more contemporary analysis (2009–2013), observed a more modest decline (relative reduction of 3.3%).29 While we observed a decrease in mortality within certain surgical cohorts, overall mortality rates remained stable. Several reasons may explain this discordance: aggregation of medical and surgical diagnosis,12–15,29 inclusion of patients with only high baseline risk of mortality or high frequency of admission,15,29 inadequate risk adjustment, and earlier periods of review.12,13,15 All these factors may affect the accuracy of temporal surgical models because surgical ICU admissions have a lower mortality than nonsurgical ICU admissions12 and a primary surgical diagnosis accounts for only 3 of the top 10 most frequent causes for ICU admissions.29 Consequently, producing accurate estimates for surgical mortality would mandate and independent analysis.
A recent Canadian review reports outcomes analogous to ours.34 Other publications have also observed decreases in ICU- and non-ICU–specific mortality following cardiac surgery,8,9,15,29 neuro/spine surgery,15,35 orthopedic surgery,15,36 and soft tissue/extremity surgery.37 This decline in mortality is attributed to the general improvements made in health care delivery, along with the development of modern medical and surgical therapies.15,29 Post-ICU mortality is an important marker of clinical care; our results aid in establishing benchmarks and help identify cohorts that may benefit from remedial quality improvement initiatives including prospective interventional studies.
Temporal Changes in ICU LOS
We detected a fractional decrease in overall ICU LOS (6% decrease, which translates to 0.06 days or 1.6 hours) along with concurrent reductions within certain surgical cohorts. Despite achieving statistical significance, the clinical relevance of such a trend remains unclear. Earlier studies showed a more significant drop in ICU LOS, but more recent studies concur with our findings which may imply a plateauing effect.15,29,38 Various political, economic, and technologic incentives such as prospective payment systems, managed care plans, innovations in health care, adherence to standard practice guidelines, enhanced rehabilitation, and mobilization efforts have all helped reduce hospital LOS; however, the complexity and ethical considerations of the ICU care have precluded it from following suit.29,38–40 Moreover, regional and local studies have demonstrated the effect of operational changes, technologic advances, and quality improvement efforts on ICU LOS reduction, but these have been yet to be demonstrated nationally.29,38,40 This may explain why recent epidemiological reviews,29 including ours, fail to demonstrate significant clinical changes to ICU LOS.
Changes in FS
A decrease in baseline FS at hospital discharge is independently associated with increased risk of postdischarge mortality; improvements are associated with increased survival.41 Along with decreases in patient mortality, we observed an increase in the rate of FS deterioration. This association may allude to the increased burden of survivorship, which is expected to grow in the United States as a result of an increasing elderly population.42 Our findings warrant concern, particularly considering the impact such deterioration would have on postdischarge outcomes.
The effect of FS deterioration on long-term outcomes has garnered national interest, and efforts to better characterize the prevalence, etiology, and associated risk factors are underway.42 Several risk factors like increased age, comorbidities, baseline cognition, prolonged immobilization, and mechanical ventilation affect both discharge FS and the propensity to develop chronic critical illness.42–44 Identifying patients at higher risk and targeting modifiable risk factors such as early mobilization may help reduce subsequent disability.42 For example, the effective implementation of the “ABCDE” bundles has helped double the rates of mobilization, independently improving FS.45 However, studies have largely analyzed the effects of poor FS; little to no research has evaluated temporal trends in changing FS.
It is possible, considering our timeline of review, that our results are reflective of trends before the clinical emphasis on chronic critical illness and early mobilization. Currently, there is heightened awareness of the clinical impact of chronic critical illness and early mobilization; expert consensus statements and critical care guidelines all currently recommend daily functional and cognitive assessments.42 Monitoring these trends in the future may more accurately reflect the effectiveness and success of such interventions. Moreover, identifying patients who are at highest risk for FS deterioration may enhance resource allocation and encourage quality improvement initiatives.
We acknowledge several limitations of our study. First, this was a retrospective review of a self-selected cohort of ICUs that used the APACHE system to audit outcomes. Despite observing great variation in geographic distribution, hospital size, and teaching status, our sample accounted for only 1.4% (n = 6,119) of all ICUs in the United States.27,46 Second, all the ICUs that met our inclusion criteria did not serially collect data for the entire 9-year duration of the study, which may have introduced bias due to data not missing completely at random. Third, our criteria for classifying patients into distinct epidemiological cohorts were based on a simple yet pragmatic clinical stratification scheme that accounted for the primary surgical team and underlying disease process. It is common clinical practice to aggregate outcomes to the primary team providing care; however, we acknowledge that, in an ICU setting, such an approach may be suboptimal because patient care is more often a multidisciplinary effort. Fourth, we were unable to capture outcomes following hospital discharge; certain outcomes including mortality and FS are known to vary significantly following discharge. For example, patients who survive hospitalizations after the initial ICU admission report higher mortality rates in the 6 months following discharge.47 To this regard, we were unable to discern the location of hospital discharge and rates of readmission, which may additionally confound our results. Several authors have observed an increasing trend to transfer patients to postacute care facilities, acute-care hospitals, or other long-term, acute-care facilities, which may all spuriously reduce hospital mortality and LOS.48,49 Fifth, we were unable to assess the accuracy of diagnostic codes and ICU prognostic tools used by the database because these may have changed with time.50 Older prediction models have documented a decrease in accuracy and calibration when used with more contemporary data sets, as a result, it is possible that the APACHE III system may have over predicted mortality outcomes.50 Sixth, as with any ecologic or epidemiologic study, we cannot discern the presence of incongruous empirical correlations at the individual or aggregate level. Nonetheless, despite these ecologic or individualistic fallacies, contemporary evidence mandates a comprehensive evaluation of multilevel models. Lastly, while we were able to adjust for center-specific random effects (between centers), we were unable to independently adjust for within center variation and other potential confounders like ICU structure (open vs. closed, medical, mixed, surgical, etc.), ICU staffing, teaching status of hospitals, use and adoption of disease-specific therapies, social determinant of health, and adherence to standard of care guidelines.
In summary, the paradigm of surgical critical care is evolving in the United States. Our epidemiological assessment demonstrates that the population of surgical patients requiring critical care services is changing over time. We identify critical trends in ICU mortality and ICU LOS and demonstrate that FS is deteriorating at an increased rate among surgical patients. Our results provide valuable insights into practice trends nationwide, help establish specialty-specific ICU benchmarks, and may have implications in health care systems planning, including resource and personnel allocation, education, and surgical training.
V.V. contributed to the study design, data analysis, data interpretation, writing, and critical revision. N.E.I. contributed to the study design, data analysis, data interpretation, writing, and critical revision. A.J.R. contributed to the study design, data interpretation, writing, and critical revision. R.F. contributed to the study design, data analysis, data interpretation, writing, and critical revision. E.F.N. contributed to the study design, data analysis, data interpretation, writing, and critical revision. K.M.P. contributed to the study design, data interpretation, writing, and critical revision. M.E.B. contributed to the study design, data interpretation, writing, and critical revision. A.C. contributed to the study design, data collection, data analysis, data interpretation, writing, and critical revision. J.G.C. contributed to the study design, data interpretation, writing, and critical revision. C.J.T. contributed to the study design, data collection, data analysis, data interpretation, writing, and critical revision.
We thank Cerner Corporation and Laura Freeseman-Freeman for providing us with the APACHE Outcomes data for research.
For all authors, no conflicts are declared. This research was supported by the National Institutes of Health's National Center for Advancing Translational Sciences, grant UL1TR002494.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health's National Center for Advancing Translational Sciences.
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CHRISTOPHER MICHETTI, M.D. (Falls Church, Virginia): Good afternoon, Dr. Kozar, Dr. Luchette, and colleagues.
Very nice presentation and excellent slides, Victor.
In this study, Vakayil and colleagues performed a retrospective epidemiological examination of surgical patient admissions to intensive care units over nine years using a robust database of a non-representative national sample of ICUs.
I believe that attempting to characterize these trends, as well as systems of care used for surgical patients in the ICU is a worthy endeavor; and I agree with the authors that such data have potential to inform system design, resource allocation, staffing, and education.
The study does have limitations due to sampling bias, however. For example, one-quarter of the patients in this cohort were cardiothoracic surgery patients. This may make it challenging for the study to achieve its stated goals. I have four questions for the authors:
Number 1. As stated in your manuscript, your sample accounts for only 1.4 percent of all ICUs in the U.S., but this was only an exploratory study. How will you address the issue of generalizability in future studies?
Number 2. In my mind, there’s a difference between the general care that a surgical patient may receive in an ICU, and the distinct entity of “surgical critical care,” because the addition of the surgical mindset and experience to the ICU care provides a unique benefit that may not be realized with all intensivists.
Can you determine from your database which kind of critical care specialists were providing the care, and what percentage of patients had no intensivist consultation at all?
Number 3. You studied patients with a primary surgical diagnosis. What percentage of patients in your study had a non-surgical primary diagnosis, or in your study ICUs had a non-surgical primary diagnosis?
If the major goal of the paper is to provide data pertinent to health care systems planning and resource allocation, it would be important to know the proportional weight of the study group among the entire ICU population.
And, Number 4. Do you have any information about the severity of illness of these patients?
For example, you showed a significant decrease in both hospital mortality and ICU length of stay in cardiac surgery and neuro/spine surgery patients over the study period, and I wonder if this is because the patients were receiving better care in the later time period, or because they were not as sick.
I think that reporting severity of illness would be essential if these data were to be used for determining benchmarks, or for resource allocation and system design.
I want to congratulate the authors on undertaking this large database investigation, and for attempting to clarify critical care needs for surgical patients, and I thank the AAST for the privilege of discussing it.
AJAI K. MALHOTRA, M.D., M.Sc., M.B.B.S. (Burlington, Vermont): Intriguing, very intriguing study and very well presented. A few questions:
What happened to the acuity of illness measured by the Apache score, and with that, how much resource utilization did change, because that data should be available?
Secondly, regarding the functional status decline, was it just a function of the increasing age of our patients?
And lastly, with the 15-hour decrease in ICU time did the ICU readmissions go up? Very interesting paper.
SUSAN EVANS, M.D. (Charlotte, North Carolina): I'm not sure I understood how you got functional outcome data.
Most people in their medical records don't have that, so maybe I just missed it, but could you help us understand that?
JOSEPH CUSCHIERI, M.D. (Seattle, Washington): Very interesting study. Just to further expand on one of the previous comments.
In regards to the types of hospitals included in this study, were they academic hospitals with or without fellowships? This is important to know because we know of the improved outcomes associated with academic institutions with fellowship training.
And finally, what was the cause of the death of the patient?
VICTOR R. VAKAYIL, M.B.B.S., M.S. (Minneapolis, Minnesota): Thank you for all those questions. I'll try to answer most of them, but I can't recollect them all.
So, the first question was on generalizability of our results. Yes, we had a total of 88 ICUs in this study, which accounted for only 1.4 percent of ICU’s nationwide. Geographically, these ICU's captured data from all four regions within the US, and included data from teaching, non-teaching and hospitals of various bed sizes. We are uncertain if the trends we observed are generalizable to the national level. Nonetheless, our is one of the first attempts to model surgical data from multiple ICU’s. We hope that our study highlights the importance of capturing ICU data at a national level. We have national databases that capture postoperative surgical outcomes such ACS-NSQIP, perhaps efforts to capture ICU outcomes may help refine and improve such epidemiological studies.
Second question was, if we were able to ascertain provider-level data and distinguish between surgical critical care services vs non-surgical, intensivist related care. This data, unfortunately, was not available to us in the database. We were unable to quantify which patient had access to intensivists services.
The third question was on non-surgical primary diagnoses. Approximately 72%, i.e. 252,000 patients had a non-surgical primary diagnosis. We only included those patients who had a primary surgical diagnosis in our analysis.
Fourth, I think we got a couple questions regarding the acuity of presentation and baseline risk, how this was measured, and adjusted for. We stratified and adjusted severity using the Apache III scoring system which accounts over 20 clinical variables and is a validated predictor of hospital mortality. Of course, this is an older scoring system but was the only scoring system available through the database. All our models were adjusted for APACHE III to produce adjusted risk-adjusted estimates.
Fifth, we had a question on resource utilization and if that changed with increasing severity of illness. This is excellent question, unfortunately we were unable to quantify this from the database; information on resource utilization was not captured.
Next, we had a couple of question on functional status; on how this was determined and captured, and additionally, if there was any association between functional status decline and increasing age. So, to my knowledge, the APACHE database has a quite a robust system for capturing data. They have trained group abstractors and clinical reviewers that collect data from patients’ charts. The specifics of the abstraction process and their quality control measure have been published elsewhere. Functional status was captured as categorical variable, both at ICU admission and at hospital discharge. This was assigned to one of three categories - independent, partially dependent and fully dependent. A designation of declined or stationary - functional status was based on the change, or lack thereof, in functional status from admission to hospital discharge. With regard to the association between functional status decline and age, we adjusted for age in our temporal models, so our results should depict adjusted estimates. We did not individually evaluate the association of increasing age and functional status. To this regard, we were unable to estimate the rate of readmission and the effect it had on our outcomes.
There was a question was on hospital-level metrics. Again, the database captures the type of hospital, bed size and geographical distribution. We had a rough mix of all types of hospitals academic, non-academic, large small, urban rural etc. If I can remember correctly, we had roughly 30 percent representation from each cohort.
The last question was on the cause of death – mortality was captured a binary variable and we were unable to evaluate cause of death.
Thank you for all those questions and for the privilege of the podium.