The prevalence of diabetes mellitus continues to increase, now affecting almost 26 million people in the United States alone.[1] Hospitalizations associated with diabetes also continue to rise,[2] and nearly 50% of the $174 billion annual costs related to diabetes care in the United States are for inpatient hospital stays.[3] In recent years, inpatient glucose control has received considerable attention, and consensus statements for glucose targets have been published.[4, 5, 6]

A number of developments support the rationale for tracking and reporting inpatient glucose control. For instance, there are clinical scenarios where treatment of hyperglycemia has been shown to lead to better patient outcomes.[6, 7, 8, 9] Second, several organizations have recognized the value of better inpatient glucose management and have developed educational resources to assist practitioners and their institutions toward achieving that goal.[10, 11, 12, 13, 14] Finally, pay‐for‐performance requirements are emerging that are relevant to inpatient diabetes management.[15, 16]

Reports on the status of inpatient glucose control in large samples of US hospitals are now becoming available, and their findings suggest differences on the basis of hospital size, hospital type, and geographic location.[17, 18] However, these reports represent cross‐sectional studies, and little is known about trends in hospital glucose control over time. To determine whether changes were occurring, we obtained inpatient point‐of‐care blood glucose (POC‐BG) data from 126 hospitals for January to December 2009 and compared these with glycemic control data collected from the same hospitals for January to December 2007,[19] separately analyzing measurements from the intensive care unit (ICU) and the non‐intensive care unit (non‐ICU).

METHODS

RESULTS

Changes in Glycemic Control

For 2007, we analyzed a total of 12,541,929 POC‐BG measurements for 1,010,705 patients, and for 2009, we analyzed a total of 10,659,418 measurements for 656,206 patients. For ICU patients, a mean of 4.6 POC‐BG measurements per day was obtained in 2009 compared to a mean of 4.7 POC‐BG measurements per day in 2007. For non‐ICU patients, the POC‐BG mean was 3.1 per day in 2009 vs 2.9 per day in 2007.

For non‐ICU data, the patient‐day‐weighted mean POC‐BG values decreased in 2009 by 5 mg/dL compared with the 2007 values (154 mg/dL vs 159 mg/dL, respectively; P < 0.001), and were clinically unchanged in the ICU data (167 mg/dL vs 166 mg/dL, respectively; P < 0.001). For non‐ICU data, the proportion of patient‐day‐weighted mean POC‐BG values in any hyperglycemia category decreased in 2009 compared with those in 2007 among all patients (all P < 0.001) (Figure 1). For the ICU data, there was no significant difference (all P > 0.20; not shown) from 2007 to 2009.

Figure 1

In the ICU data, 2.9% of patient days on average had at least 1 POC‐BG value <70 mg/dL in both 2007 and 2009 (P = 0.67). There were fewer patient days with values <40 mg/dL in 2009 (1.1%) compared to 2007 (1.4%) in the ICU (P < 0.001). In the non‐ICU data, the mean percentage of patient days with a value <70 mg/dL was higher in 2009 (5.1%) than in 2007 (4.7%) (P < 0.001); however, there were actually fewer patient days in 2009 on average with a value <40 mg/dL (0.84% vs 1.1% for 2009 vs 2007; P < 0.001).

DISCUSSION

Optimal management of hospital hyperglycemia is now advocated by a number of professional societies and organizations.[10, 11, 12, 13] One of the next major tasks in the area of inpatient diabetes management will be how to identify and evaluate changes in glycemic control among US hospitals over time. Respondents to a recent survey of hospitals indicated that most institutions are now attempting to initiate quality improvement programs for the management of inpatients with diabetes.[21] These initiatives may translate into objective changes that could be monitored on a national level. However, few data exist on trends in glucose control in US hospitals. In our analysis, POC‐BG data from 126 hospitals collected in 2009 were compared to data obtained from the same hospitals in 2007. Our findings, and the methods of data collection and analysis described previously,[18, 19] demonstrate how such data can be used as a national benchmarking process for inpatient glucose control.

At all levels of hyperglycemia, significant decreases in patient‐day‐weighted mean values were found in non‐ICU data but not in ICU data. During the time these data were collected, recommendations about glucose targets in the critically ill were in a state of flux.[22, 23, 24, 25, 26, 27] Thus, the lack of hyperglycemia improvement in the ICU data between 2007 and 2009 may reflect the reluctance of providers to aggressively manage hyperglycemia because of recent reports linking increased mortality to tight glucose control.[25, 28, 29, 30] The differences in patient‐day‐weighted mean glucose values detected in the non‐ICU data between the 2 analytic periods were statistically significant, but were otherwise small and may not have clinical implications as far as an association with improved patient outcomes. Ongoing longitudinal analysis is required to establish whether these improvements in non‐ICU glucose control will persist over time.

Changes in glycemic control between the 2 periods were also noted when data were stratified according to hospital characteristics. Differences in glucose control in ICU data were not consistently better or worse, but varied by category of hospital characteristics (hospital size, hospital type, and geographic region). Other than academic hospitals and hospitals in the West, changes in the ICU data were small and likely do not have clinical importance. Analysis of non‐ICU data, however, showed consistent improvement within all 3 categories. Some hospital characteristics did change between the 2 study periods: there were fewer hospitals with <200 beds, more hospitals with 200 to 299 beds, a decrease in hospitals identified as rural, and an increase in hospitals designated as urban. Our previous analyses have indicated that hospital characteristics should be considered when examining national inpatient glucose data.[18, 19] In this analysis there was a statistically significant interaction between the year for which data were analyzed and each category of hospital characteristics. It is unclear how these evolving characteristics could have impacted inpatient glucose control. A change in hospital characteristics may in fact represent a change in resources to manage inpatient hyperglycemia. Future studies with nationally aggregated inpatient glucose data that assess longitudinal changes in glucose data may also have to account for variations in hospital characteristics over time in addition to the characteristics of the hospitals themselves.

Differences in hypoglycemia frequency, as calculated as the proportion of patient hospital days, were also detected. In the ICU data, the percentage of days with at least 1 value <70 mg/dL was similar between 2007 and 2009, but the proportion of days with at least 1 value <40 mg/dL was less in 2009, suggesting that institutions as a whole in this analysis may have been more focused on reducing the frequency of severe hypoglycemia. However, in the non‐ICU, there were more days in 2009 with a value <70 mg/dL, but fewer with a value <40 mg/dL. In noncritically ill patients, institutions likely continue to attempt to find the best balance between optimizing glycemic control while minimizing the risk of hypoglycemia. It should be pointed out, however, that overall, the frequency of hypoglycemia, particularly severe hypoglycemia, was quite low in this analysis, as it has been in our previous reports.[18, 19] An examination of hypoglycemia frequency by hospital characteristic to evaluate differences in this metric would be of interest in a future analysis.

The limitations of these data have been previously outlined,[18, 19] and they include the lack of patient‐level data such as demographics and the lack of information on diagnoses that allow adjustment of comparisons by the severity of illness. Moreover, without detailed treatment‐specific information (such as type of insulin protocol), one cannot establish the basis for longitudinal differences in glucose control. Volunteer‐dependent hospital involvement that creates selection bias may skew data toward those who are aware that they are witnessing a successful reduction in hyperglycemia. Finally, POC‐BG may not be the optimal method for assessing glycemic control. The limitations of current methods of evaluating inpatient glycemic control were recently reviewed.[31] Nonetheless, POC‐BG measurements remain the richest source of data on hospital hyperglycemia because of their widespread use and large sample size. A data warehouse of nearly 600 hospitals now exists,[18] which will permit future longitudinal analyses of glucose control in even larger samples.

Despite such limitations, our findings do represent the first analysis of trends in glucose control in a large cross‐section of US hospitals. Over 2 years, non‐ICU hyperglycemia improved among hospitals of all sizes and types and in all regions, whereas similar improvement did not occur in ICU hyperglycemia. Continued analysis will determine whether these trends continue. For those hospitals that are achieving better glucose control in non‐ICU patients, more information is needed on how they are accomplishing this so that protocols can be standardized and disseminated.

The prevalence of diabetes mellitus continues to increase, now affecting almost 26 million people in the United States alone.[1] Hospitalizations associated with diabetes also continue to rise,[2] and nearly 50% of the $174 billion annual costs related to diabetes care in the United States are for inpatient hospital stays.[3] In recent years, inpatient glucose control has received considerable attention, and consensus statements for glucose targets have been published.[4, 5, 6]

A number of developments support the rationale for tracking and reporting inpatient glucose control. For instance, there are clinical scenarios where treatment of hyperglycemia has been shown to lead to better patient outcomes.[6, 7, 8, 9] Second, several organizations have recognized the value of better inpatient glucose management and have developed educational resources to assist practitioners and their institutions toward achieving that goal.[10, 11, 12, 13, 14] Finally, pay‐for‐performance requirements are emerging that are relevant to inpatient diabetes management.[15, 16]

Reports on the status of inpatient glucose control in large samples of US hospitals are now becoming available, and their findings suggest differences on the basis of hospital size, hospital type, and geographic location.[17, 18] However, these reports represent cross‐sectional studies, and little is known about trends in hospital glucose control over time. To determine whether changes were occurring, we obtained inpatient point‐of‐care blood glucose (POC‐BG) data from 126 hospitals for January to December 2009 and compared these with glycemic control data collected from the same hospitals for January to December 2007,[19] separately analyzing measurements from the intensive care unit (ICU) and the non‐intensive care unit (non‐ICU).

METHODS

RESULTS

Changes in Glycemic Control

For 2007, we analyzed a total of 12,541,929 POC‐BG measurements for 1,010,705 patients, and for 2009, we analyzed a total of 10,659,418 measurements for 656,206 patients. For ICU patients, a mean of 4.6 POC‐BG measurements per day was obtained in 2009 compared to a mean of 4.7 POC‐BG measurements per day in 2007. For non‐ICU patients, the POC‐BG mean was 3.1 per day in 2009 vs 2.9 per day in 2007.

For non‐ICU data, the patient‐day‐weighted mean POC‐BG values decreased in 2009 by 5 mg/dL compared with the 2007 values (154 mg/dL vs 159 mg/dL, respectively; P < 0.001), and were clinically unchanged in the ICU data (167 mg/dL vs 166 mg/dL, respectively; P < 0.001). For non‐ICU data, the proportion of patient‐day‐weighted mean POC‐BG values in any hyperglycemia category decreased in 2009 compared with those in 2007 among all patients (all P < 0.001) (Figure 1). For the ICU data, there was no significant difference (all P > 0.20; not shown) from 2007 to 2009.

Figure 1

In the ICU data, 2.9% of patient days on average had at least 1 POC‐BG value <70 mg/dL in both 2007 and 2009 (P = 0.67). There were fewer patient days with values <40 mg/dL in 2009 (1.1%) compared to 2007 (1.4%) in the ICU (P < 0.001). In the non‐ICU data, the mean percentage of patient days with a value <70 mg/dL was higher in 2009 (5.1%) than in 2007 (4.7%) (P < 0.001); however, there were actually fewer patient days in 2009 on average with a value <40 mg/dL (0.84% vs 1.1% for 2009 vs 2007; P < 0.001).

DISCUSSION

Optimal management of hospital hyperglycemia is now advocated by a number of professional societies and organizations.[10, 11, 12, 13] One of the next major tasks in the area of inpatient diabetes management will be how to identify and evaluate changes in glycemic control among US hospitals over time. Respondents to a recent survey of hospitals indicated that most institutions are now attempting to initiate quality improvement programs for the management of inpatients with diabetes.[21] These initiatives may translate into objective changes that could be monitored on a national level. However, few data exist on trends in glucose control in US hospitals. In our analysis, POC‐BG data from 126 hospitals collected in 2009 were compared to data obtained from the same hospitals in 2007. Our findings, and the methods of data collection and analysis described previously,[18, 19] demonstrate how such data can be used as a national benchmarking process for inpatient glucose control.

At all levels of hyperglycemia, significant decreases in patient‐day‐weighted mean values were found in non‐ICU data but not in ICU data. During the time these data were collected, recommendations about glucose targets in the critically ill were in a state of flux.[22, 23, 24, 25, 26, 27] Thus, the lack of hyperglycemia improvement in the ICU data between 2007 and 2009 may reflect the reluctance of providers to aggressively manage hyperglycemia because of recent reports linking increased mortality to tight glucose control.[25, 28, 29, 30] The differences in patient‐day‐weighted mean glucose values detected in the non‐ICU data between the 2 analytic periods were statistically significant, but were otherwise small and may not have clinical implications as far as an association with improved patient outcomes. Ongoing longitudinal analysis is required to establish whether these improvements in non‐ICU glucose control will persist over time.

Changes in glycemic control between the 2 periods were also noted when data were stratified according to hospital characteristics. Differences in glucose control in ICU data were not consistently better or worse, but varied by category of hospital characteristics (hospital size, hospital type, and geographic region). Other than academic hospitals and hospitals in the West, changes in the ICU data were small and likely do not have clinical importance. Analysis of non‐ICU data, however, showed consistent improvement within all 3 categories. Some hospital characteristics did change between the 2 study periods: there were fewer hospitals with <200 beds, more hospitals with 200 to 299 beds, a decrease in hospitals identified as rural, and an increase in hospitals designated as urban. Our previous analyses have indicated that hospital characteristics should be considered when examining national inpatient glucose data.[18, 19] In this analysis there was a statistically significant interaction between the year for which data were analyzed and each category of hospital characteristics. It is unclear how these evolving characteristics could have impacted inpatient glucose control. A change in hospital characteristics may in fact represent a change in resources to manage inpatient hyperglycemia. Future studies with nationally aggregated inpatient glucose data that assess longitudinal changes in glucose data may also have to account for variations in hospital characteristics over time in addition to the characteristics of the hospitals themselves.

Differences in hypoglycemia frequency, as calculated as the proportion of patient hospital days, were also detected. In the ICU data, the percentage of days with at least 1 value <70 mg/dL was similar between 2007 and 2009, but the proportion of days with at least 1 value <40 mg/dL was less in 2009, suggesting that institutions as a whole in this analysis may have been more focused on reducing the frequency of severe hypoglycemia. However, in the non‐ICU, there were more days in 2009 with a value <70 mg/dL, but fewer with a value <40 mg/dL. In noncritically ill patients, institutions likely continue to attempt to find the best balance between optimizing glycemic control while minimizing the risk of hypoglycemia. It should be pointed out, however, that overall, the frequency of hypoglycemia, particularly severe hypoglycemia, was quite low in this analysis, as it has been in our previous reports.[18, 19] An examination of hypoglycemia frequency by hospital characteristic to evaluate differences in this metric would be of interest in a future analysis.

The limitations of these data have been previously outlined,[18, 19] and they include the lack of patient‐level data such as demographics and the lack of information on diagnoses that allow adjustment of comparisons by the severity of illness. Moreover, without detailed treatment‐specific information (such as type of insulin protocol), one cannot establish the basis for longitudinal differences in glucose control. Volunteer‐dependent hospital involvement that creates selection bias may skew data toward those who are aware that they are witnessing a successful reduction in hyperglycemia. Finally, POC‐BG may not be the optimal method for assessing glycemic control. The limitations of current methods of evaluating inpatient glycemic control were recently reviewed.[31] Nonetheless, POC‐BG measurements remain the richest source of data on hospital hyperglycemia because of their widespread use and large sample size. A data warehouse of nearly 600 hospitals now exists,[18] which will permit future longitudinal analyses of glucose control in even larger samples.

Despite such limitations, our findings do represent the first analysis of trends in glucose control in a large cross‐section of US hospitals. Over 2 years, non‐ICU hyperglycemia improved among hospitals of all sizes and types and in all regions, whereas similar improvement did not occur in ICU hyperglycemia. Continued analysis will determine whether these trends continue. For those hospitals that are achieving better glucose control in non‐ICU patients, more information is needed on how they are accomplishing this so that protocols can be standardized and disseminated.

The prevalence of diabetes mellitus continues to increase, now affecting almost 26 million people in the United States alone.[1] Hospitalizations associated with diabetes also continue to rise,[2] and nearly 50% of the $174 billion annual costs related to diabetes care in the United States are for inpatient hospital stays.[3] In recent years, inpatient glucose control has received considerable attention, and consensus statements for glucose targets have been published.[4, 5, 6]

A number of developments support the rationale for tracking and reporting inpatient glucose control. For instance, there are clinical scenarios where treatment of hyperglycemia has been shown to lead to better patient outcomes.[6, 7, 8, 9] Second, several organizations have recognized the value of better inpatient glucose management and have developed educational resources to assist practitioners and their institutions toward achieving that goal.[10, 11, 12, 13, 14] Finally, pay‐for‐performance requirements are emerging that are relevant to inpatient diabetes management.[15, 16]

Reports on the status of inpatient glucose control in large samples of US hospitals are now becoming available, and their findings suggest differences on the basis of hospital size, hospital type, and geographic location.[17, 18] However, these reports represent cross‐sectional studies, and little is known about trends in hospital glucose control over time. To determine whether changes were occurring, we obtained inpatient point‐of‐care blood glucose (POC‐BG) data from 126 hospitals for January to December 2009 and compared these with glycemic control data collected from the same hospitals for January to December 2007,[19] separately analyzing measurements from the intensive care unit (ICU) and the non‐intensive care unit (non‐ICU).

METHODS

RESULTS

Changes in Glycemic Control

For 2007, we analyzed a total of 12,541,929 POC‐BG measurements for 1,010,705 patients, and for 2009, we analyzed a total of 10,659,418 measurements for 656,206 patients. For ICU patients, a mean of 4.6 POC‐BG measurements per day was obtained in 2009 compared to a mean of 4.7 POC‐BG measurements per day in 2007. For non‐ICU patients, the POC‐BG mean was 3.1 per day in 2009 vs 2.9 per day in 2007.

For non‐ICU data, the patient‐day‐weighted mean POC‐BG values decreased in 2009 by 5 mg/dL compared with the 2007 values (154 mg/dL vs 159 mg/dL, respectively; P < 0.001), and were clinically unchanged in the ICU data (167 mg/dL vs 166 mg/dL, respectively; P < 0.001). For non‐ICU data, the proportion of patient‐day‐weighted mean POC‐BG values in any hyperglycemia category decreased in 2009 compared with those in 2007 among all patients (all P < 0.001) (Figure 1). For the ICU data, there was no significant difference (all P > 0.20; not shown) from 2007 to 2009.

Figure 1

In the ICU data, 2.9% of patient days on average had at least 1 POC‐BG value <70 mg/dL in both 2007 and 2009 (P = 0.67). There were fewer patient days with values <40 mg/dL in 2009 (1.1%) compared to 2007 (1.4%) in the ICU (P < 0.001). In the non‐ICU data, the mean percentage of patient days with a value <70 mg/dL was higher in 2009 (5.1%) than in 2007 (4.7%) (P < 0.001); however, there were actually fewer patient days in 2009 on average with a value <40 mg/dL (0.84% vs 1.1% for 2009 vs 2007; P < 0.001).

DISCUSSION

Optimal management of hospital hyperglycemia is now advocated by a number of professional societies and organizations.[10, 11, 12, 13] One of the next major tasks in the area of inpatient diabetes management will be how to identify and evaluate changes in glycemic control among US hospitals over time. Respondents to a recent survey of hospitals indicated that most institutions are now attempting to initiate quality improvement programs for the management of inpatients with diabetes.[21] These initiatives may translate into objective changes that could be monitored on a national level. However, few data exist on trends in glucose control in US hospitals. In our analysis, POC‐BG data from 126 hospitals collected in 2009 were compared to data obtained from the same hospitals in 2007. Our findings, and the methods of data collection and analysis described previously,[18, 19] demonstrate how such data can be used as a national benchmarking process for inpatient glucose control.

At all levels of hyperglycemia, significant decreases in patient‐day‐weighted mean values were found in non‐ICU data but not in ICU data. During the time these data were collected, recommendations about glucose targets in the critically ill were in a state of flux.[22, 23, 24, 25, 26, 27] Thus, the lack of hyperglycemia improvement in the ICU data between 2007 and 2009 may reflect the reluctance of providers to aggressively manage hyperglycemia because of recent reports linking increased mortality to tight glucose control.[25, 28, 29, 30] The differences in patient‐day‐weighted mean glucose values detected in the non‐ICU data between the 2 analytic periods were statistically significant, but were otherwise small and may not have clinical implications as far as an association with improved patient outcomes. Ongoing longitudinal analysis is required to establish whether these improvements in non‐ICU glucose control will persist over time.

Changes in glycemic control between the 2 periods were also noted when data were stratified according to hospital characteristics. Differences in glucose control in ICU data were not consistently better or worse, but varied by category of hospital characteristics (hospital size, hospital type, and geographic region). Other than academic hospitals and hospitals in the West, changes in the ICU data were small and likely do not have clinical importance. Analysis of non‐ICU data, however, showed consistent improvement within all 3 categories. Some hospital characteristics did change between the 2 study periods: there were fewer hospitals with <200 beds, more hospitals with 200 to 299 beds, a decrease in hospitals identified as rural, and an increase in hospitals designated as urban. Our previous analyses have indicated that hospital characteristics should be considered when examining national inpatient glucose data.[18, 19] In this analysis there was a statistically significant interaction between the year for which data were analyzed and each category of hospital characteristics. It is unclear how these evolving characteristics could have impacted inpatient glucose control. A change in hospital characteristics may in fact represent a change in resources to manage inpatient hyperglycemia. Future studies with nationally aggregated inpatient glucose data that assess longitudinal changes in glucose data may also have to account for variations in hospital characteristics over time in addition to the characteristics of the hospitals themselves.

Differences in hypoglycemia frequency, as calculated as the proportion of patient hospital days, were also detected. In the ICU data, the percentage of days with at least 1 value <70 mg/dL was similar between 2007 and 2009, but the proportion of days with at least 1 value <40 mg/dL was less in 2009, suggesting that institutions as a whole in this analysis may have been more focused on reducing the frequency of severe hypoglycemia. However, in the non‐ICU, there were more days in 2009 with a value <70 mg/dL, but fewer with a value <40 mg/dL. In noncritically ill patients, institutions likely continue to attempt to find the best balance between optimizing glycemic control while minimizing the risk of hypoglycemia. It should be pointed out, however, that overall, the frequency of hypoglycemia, particularly severe hypoglycemia, was quite low in this analysis, as it has been in our previous reports.[18, 19] An examination of hypoglycemia frequency by hospital characteristic to evaluate differences in this metric would be of interest in a future analysis.

The limitations of these data have been previously outlined,[18, 19] and they include the lack of patient‐level data such as demographics and the lack of information on diagnoses that allow adjustment of comparisons by the severity of illness. Moreover, without detailed treatment‐specific information (such as type of insulin protocol), one cannot establish the basis for longitudinal differences in glucose control. Volunteer‐dependent hospital involvement that creates selection bias may skew data toward those who are aware that they are witnessing a successful reduction in hyperglycemia. Finally, POC‐BG may not be the optimal method for assessing glycemic control. The limitations of current methods of evaluating inpatient glycemic control were recently reviewed.[31] Nonetheless, POC‐BG measurements remain the richest source of data on hospital hyperglycemia because of their widespread use and large sample size. A data warehouse of nearly 600 hospitals now exists,[18] which will permit future longitudinal analyses of glucose control in even larger samples.

Despite such limitations, our findings do represent the first analysis of trends in glucose control in a large cross‐section of US hospitals. Over 2 years, non‐ICU hyperglycemia improved among hospitals of all sizes and types and in all regions, whereas similar improvement did not occur in ICU hyperglycemia. Continued analysis will determine whether these trends continue. For those hospitals that are achieving better glucose control in non‐ICU patients, more information is needed on how they are accomplishing this so that protocols can be standardized and disseminated.

Favorable health outcomes are more likely to occur when the healthcare team quickly identifies and responds to patients at risk.[1, 2, 3] However, the treatment process can break down during handoffs if the clinical condition and active issues are not well communicated.[4] Patients whose decline cannot be reversed also challenge the health team. Many are referred to hospice late,[5] and some do not receive the type of end‐of‐life care matching their preferences.[6]

Progress toward the elusive goal of more effective and efficient care might be made via an industrial engineering approach, mass customization, in which bundles of services are delivered based on the anticipated needs of subsets of patients.[7, 8] An underlying rationale is the frequent finding that a small proportion of individuals experiences the majority of the events of interest, commonly referenced as the Pareto principle.[7] Clinical prediction rules can help identify these high‐risk subsets.[9] However, as more condition‐specific rules become available, the clinical team faces logistical challenges when attempting to incorporate these into practice. For example, which team member will be responsible for generating the prediction and communicating the level of risk? What actions should follow for a given level of risk? What should be done for patients with conditions not addressed by an existing rule?

In this study, we present our rationale for health systems to implement a process for generating mortality predictions at the time of admission on most, if not all, adult patients as a context for the activities of the various clinical team members. Recent studies demonstrate that in‐hospital or 30‐day mortality can be predicted with substantial accuracy using information available at the time of admission.[10, 11, 12, 13, 14, 15, 16, 17, 18, 19] Relationships are beginning to be explored among the risk factors for mortality and other outcomes such as length of stay, unplanned transfers to intensive care units, 30‐day readmissions, and extended care facility placement.[10, 20, 21, 22] We extend this work by examining how a number of adverse events can be understood through their relationship with the risk of dying. We begin by deriving and validating a new mortality prediction rule using information feasible for our institution to use in its implementation.

METHODS

The prediction rule was derived from data on all inpatients (n = 56,003) 18 to 99 years old from St. Joseph Mercy Hospital, Ann Arbor from 2008 to 2009. This is a community‐based, tertiary‐care center. We reference derivation cases as D1, validation cases from the same hospital in the following year (2010) as V1, and data from a second hospital in 2010 as V2. The V2 hospital belonged to the same parent health corporation and shared some physician specialists with D1 and V1 but had separate medical and nursing staff.

The primary outcome predicted is 30‐day mortality from the time of admission. We chose 30‐day rather than in‐hospital mortality to address concerns of potential confounding of duration of hospital stay and likelihood of dying in the hospital.[23] Risk factors were considered for inclusion into the prediction rule based on their prevalence, conceptual, and univariable association with death (details provided in the Supporting information, Appendix I and II, in the online version of this article). The types of risk factors considered were patient diagnoses as of the time of admission obtained from hospital administrative data and grouped by the 2011 Clinical Classification Software (http://www.hcupus.ahrq.gov/toolssoftware/ccs/ccs.jsp#download, accessed June 6, 2012), administrative data from previous hospitalizations within the health system in the preceding 12 months, and the worst value of clinical laboratory blood tests obtained within 30 days prior to the time of admission. When a given patient had missing values for the laboratory tests of interest, we imputed a normal value, assuming the clinician had not ordered these tests because he/she expected the patient would have normal results. The imputed normal values were derived from available results from patients discharged alive with short hospital stays (3 days) in 2007 to 2008. The datasets were built and analyzed using SAS version 9.1, 9.2 (SAS Institute, Inc., Cary, NC) and R (R Foundation for Statistical Computing, Vienna, Austria; http://www.R‐project.org).

RESULTS

Table 1 displays the risk factors used in the 30‐day mortality prediction rule, their distribution in the populations of interest, and the frequency of the outcomes of interest. The derivation (D1) and validation (V1) populations were clinically similar; the patients of hospital V2 differed in the proportion of risk factors and outcomes. The scoring weights or parameter estimates for the risk factors are given in the Appendix (see Supporting Information, Appendix I, in the online version of this article).

Predicting 30‐Day Mortality

The areas under the ROC (95% confidence interval [CI]) for the D1, V1, and V2 populations were 0.876 (95% CI, 0.870‐0.882), 0.885 (95% CI, 0.877‐0.893), and 0.883 (95% CI, 0.875‐0.892), respectively. The calibration curves for all 3 populations are shown in Figure 1. The overlap of symbols indicates that the level of predicted risk matched actual mortality for most intervals, with slight underprediction for those in the highest risk percentiles.

Figure 1

Example of Risk Strata

Figure 2 displays the relationship between the predicted probability of dying within 30 days and the outcomes of interest for V1, and illustrates the Pareto principle for defining high‐ and low‐risk subgroups. Most of the 30‐day deaths (74.7% of D1, 74.2% of V1, and 85.3% of V2) occurred in the small subset of patients with a predicted probability of death exceeding 0.067 (the top quintile of risk of D1, the top 18 % of V1, and the top 29.8% of V2). In contrast, the mortality rate for those with a predicted risk of 0.0033 was 0.02% for the lowest quintile of risk in D1, 0.07% for the 19.3% having the lowest risk in V1, and 0% for the 9.7% of patients with the lowest risk in V2. Figure 3 indicates that the risk for dying peaks within the first few days of the hospitalization. Moreover, those in the high‐risk group remained at elevated risk relative to the lower risk strata for at least 100 days.

Figure 2

Figure 3

DISCUSSION

The primary contribution of our work concerns the number and strength of associations between the probability of dying within 30 days and other events, and the implications for organizing the healthcare delivery model. We also add to the growing evidence that death within 30 days can be accurately predicted at the time of admission from demographic information, modest levels of diagnostic information, and clinical laboratory values. We developed a new prediction rule with excellent accuracy that compares well to a rule recently developed by the Kaiser Permanente system.[13, 14] Feasibility considerations are likely to be the ultimate determinant of which prediction rule a health system chooses.[13, 14, 29] An independent evaluation of the candidate rules applied to the same data is required to compare their accuracy.

These results suggest a context for the coordination of clinical care processes, although mortality risk is not the only domain health systems must address. For illustrative purposes, we will refer to the risk strata shown in Figure 2. After the decisions to admit the patient to the hospital and whether or not surgical intervention is needed, the next decision concerns the level and type of nursing care needed.[10] Recent studies continue to show challenges both with unplanned transfers to intensive care units[21] and care delivered that is consistently concordant with patient wishes.[6, 30] The level of risk for multiple adverse outcomes suggests stratum 1 patients would be the priority group for perfecting the placement and preference assessment process. Our institution is currently piloting an internal placement guideline recommending that nonpalliative patients in the top 2.5 percentile of mortality risk be placed initially in either an intensive or intermediate care unit to receive the potential benefit of higher nursing staffing levels.[31] However, mortality risk cannot be the only criterion used for placement, as demonstrated by its relatively weak association with overall ICU utilization. Our findings may reflect the role of unmeasured factors such as the need for mechanical ventilation, patient preference for comfort care, bed availability, change in patient condition after admission, and inconsistent application of admission criteria.[17, 21, 32, 33, 34]

After the placement decision, the team could decide if the usual level of monitoring, physician rounding, and care coordination would be adequate for the level of risk or whether an additional anticipatory approach is needed. The weak relationship between the risk of death and incidence of complications, although not a new finding,[35, 36] suggests routine surveillance activities need to be conducted on all patients regardless of risk to detect a complication, but that a rescue plan be developed in advance for high mortality risk patients, for example strata 1 and 2, in the event they should develop a complication.[36] Inclusion of the patient's risk strata as part of the routine hand‐off communication among hospitalists, nurses, and other team members could provide a succinct common alert for the likelihood of adverse events.

The 30‐day mortality risk also informs the transition care plan following hospitalization, given the strong association with death in 180 days and the persistent level of this risk (Figure 3). Again, communication of the risk status (stratum 1) to the team caring for the patient after the hospitalization provides a common reference for prognosis and level of attention needed. However, the prediction accuracy is not sufficient to refer high‐risk patients into hospice, but rather, to identify the high‐risk subset having the most urgent need to have their preferences for future end‐of‐life care understood and addressed. The weak relationship of mortality risk with 30‐day readmissions indicates that our rule would have a limited role in identifying readmission risk per se. Others have noted the difficulty in accurately predicting readmissions, most likely because the underlying causes are multifactorial.[37] Our results suggest that 1 dynamic for readmission is the risk of dying, and so the underlying causes of this risk should be addressed in the transition plan.

There are a number of limitations with our study. First, this rule was developed and validated on data from only 2 institutions, assembled retrospectively, with diagnostic information determined from administrative data. One cannot assume the accuracy will carry over to other institutions[29] or when there is diagnostic uncertainty at the time of admission. Second, the 30‐day mortality risk should not be used as the sole criterion for determining the service intensity for individual patients because of issues with calibration, interpretation of risk, and confounding. The calibration curves (Figure 2) show the slight underprediction of the risk of dying for high‐risk groups. Other studies have also noted problems with precise calibration in validation datasets.[13, 14] Caution is also needed in the interpretation of what it means to be at high risk. Most patients in stratum 1 were alive at 30 days; therefore, being at high risk is not a death sentence. Furthermore, the relative weights of the risk factors reflect (ie, are confounded by) the level of treatment rendered. Some deaths within the higher‐risk percentiles undoubtedly occurred in patients choosing a palliative rather than a curative approach, perhaps partially explaining the slight underprediction of deaths. Conversely, the low mortality experienced by patients within the lower‐risk strata may indicate the treatment provided was effective. Low mortality risk does not imply less care is needed.

A third limitation is that we have not defined the thresholds of risk that should trigger placement and care intensity, although we provide examples on how this could be done. Each institution will need to calibrate the thresholds and associated decision‐making processes according to its own environment.[14] Interested readers can explore the sensitivity and specificity of various thresholds\ by using the tables in the Appendix (see the Supporting information, Appendix II, in the online version of this article). Finally, we do not know if identifying the mortality risk on admission will lead to better outcomes[19, 29]

CONCLUSIONS

Death within 30 days can be predicted with information known at the time of admission, and is associated with the risk of having other adverse events. We believe the probability of death can be used to define strata of risk that provide a succinct common reference point for the multidisciplinary team to anticipate the clinical course of subsets of patients and intervene with proportional intensity.

Favorable health outcomes are more likely to occur when the healthcare team quickly identifies and responds to patients at risk.[1, 2, 3] However, the treatment process can break down during handoffs if the clinical condition and active issues are not well communicated.[4] Patients whose decline cannot be reversed also challenge the health team. Many are referred to hospice late,[5] and some do not receive the type of end‐of‐life care matching their preferences.[6]

Progress toward the elusive goal of more effective and efficient care might be made via an industrial engineering approach, mass customization, in which bundles of services are delivered based on the anticipated needs of subsets of patients.[7, 8] An underlying rationale is the frequent finding that a small proportion of individuals experiences the majority of the events of interest, commonly referenced as the Pareto principle.[7] Clinical prediction rules can help identify these high‐risk subsets.[9] However, as more condition‐specific rules become available, the clinical team faces logistical challenges when attempting to incorporate these into practice. For example, which team member will be responsible for generating the prediction and communicating the level of risk? What actions should follow for a given level of risk? What should be done for patients with conditions not addressed by an existing rule?

In this study, we present our rationale for health systems to implement a process for generating mortality predictions at the time of admission on most, if not all, adult patients as a context for the activities of the various clinical team members. Recent studies demonstrate that in‐hospital or 30‐day mortality can be predicted with substantial accuracy using information available at the time of admission.[10, 11, 12, 13, 14, 15, 16, 17, 18, 19] Relationships are beginning to be explored among the risk factors for mortality and other outcomes such as length of stay, unplanned transfers to intensive care units, 30‐day readmissions, and extended care facility placement.[10, 20, 21, 22] We extend this work by examining how a number of adverse events can be understood through their relationship with the risk of dying. We begin by deriving and validating a new mortality prediction rule using information feasible for our institution to use in its implementation.

METHODS

The prediction rule was derived from data on all inpatients (n = 56,003) 18 to 99 years old from St. Joseph Mercy Hospital, Ann Arbor from 2008 to 2009. This is a community‐based, tertiary‐care center. We reference derivation cases as D1, validation cases from the same hospital in the following year (2010) as V1, and data from a second hospital in 2010 as V2. The V2 hospital belonged to the same parent health corporation and shared some physician specialists with D1 and V1 but had separate medical and nursing staff.

The primary outcome predicted is 30‐day mortality from the time of admission. We chose 30‐day rather than in‐hospital mortality to address concerns of potential confounding of duration of hospital stay and likelihood of dying in the hospital.[23] Risk factors were considered for inclusion into the prediction rule based on their prevalence, conceptual, and univariable association with death (details provided in the Supporting information, Appendix I and II, in the online version of this article). The types of risk factors considered were patient diagnoses as of the time of admission obtained from hospital administrative data and grouped by the 2011 Clinical Classification Software (http://www.hcupus.ahrq.gov/toolssoftware/ccs/ccs.jsp#download, accessed June 6, 2012), administrative data from previous hospitalizations within the health system in the preceding 12 months, and the worst value of clinical laboratory blood tests obtained within 30 days prior to the time of admission. When a given patient had missing values for the laboratory tests of interest, we imputed a normal value, assuming the clinician had not ordered these tests because he/she expected the patient would have normal results. The imputed normal values were derived from available results from patients discharged alive with short hospital stays (3 days) in 2007 to 2008. The datasets were built and analyzed using SAS version 9.1, 9.2 (SAS Institute, Inc., Cary, NC) and R (R Foundation for Statistical Computing, Vienna, Austria; http://www.R‐project.org).

RESULTS

Table 1 displays the risk factors used in the 30‐day mortality prediction rule, their distribution in the populations of interest, and the frequency of the outcomes of interest. The derivation (D1) and validation (V1) populations were clinically similar; the patients of hospital V2 differed in the proportion of risk factors and outcomes. The scoring weights or parameter estimates for the risk factors are given in the Appendix (see Supporting Information, Appendix I, in the online version of this article).

Predicting 30‐Day Mortality

The areas under the ROC (95% confidence interval [CI]) for the D1, V1, and V2 populations were 0.876 (95% CI, 0.870‐0.882), 0.885 (95% CI, 0.877‐0.893), and 0.883 (95% CI, 0.875‐0.892), respectively. The calibration curves for all 3 populations are shown in Figure 1. The overlap of symbols indicates that the level of predicted risk matched actual mortality for most intervals, with slight underprediction for those in the highest risk percentiles.

Figure 1

Example of Risk Strata

Figure 2 displays the relationship between the predicted probability of dying within 30 days and the outcomes of interest for V1, and illustrates the Pareto principle for defining high‐ and low‐risk subgroups. Most of the 30‐day deaths (74.7% of D1, 74.2% of V1, and 85.3% of V2) occurred in the small subset of patients with a predicted probability of death exceeding 0.067 (the top quintile of risk of D1, the top 18 % of V1, and the top 29.8% of V2). In contrast, the mortality rate for those with a predicted risk of 0.0033 was 0.02% for the lowest quintile of risk in D1, 0.07% for the 19.3% having the lowest risk in V1, and 0% for the 9.7% of patients with the lowest risk in V2. Figure 3 indicates that the risk for dying peaks within the first few days of the hospitalization. Moreover, those in the high‐risk group remained at elevated risk relative to the lower risk strata for at least 100 days.

Figure 2

Figure 3

DISCUSSION

The primary contribution of our work concerns the number and strength of associations between the probability of dying within 30 days and other events, and the implications for organizing the healthcare delivery model. We also add to the growing evidence that death within 30 days can be accurately predicted at the time of admission from demographic information, modest levels of diagnostic information, and clinical laboratory values. We developed a new prediction rule with excellent accuracy that compares well to a rule recently developed by the Kaiser Permanente system.[13, 14] Feasibility considerations are likely to be the ultimate determinant of which prediction rule a health system chooses.[13, 14, 29] An independent evaluation of the candidate rules applied to the same data is required to compare their accuracy.

These results suggest a context for the coordination of clinical care processes, although mortality risk is not the only domain health systems must address. For illustrative purposes, we will refer to the risk strata shown in Figure 2. After the decisions to admit the patient to the hospital and whether or not surgical intervention is needed, the next decision concerns the level and type of nursing care needed.[10] Recent studies continue to show challenges both with unplanned transfers to intensive care units[21] and care delivered that is consistently concordant with patient wishes.[6, 30] The level of risk for multiple adverse outcomes suggests stratum 1 patients would be the priority group for perfecting the placement and preference assessment process. Our institution is currently piloting an internal placement guideline recommending that nonpalliative patients in the top 2.5 percentile of mortality risk be placed initially in either an intensive or intermediate care unit to receive the potential benefit of higher nursing staffing levels.[31] However, mortality risk cannot be the only criterion used for placement, as demonstrated by its relatively weak association with overall ICU utilization. Our findings may reflect the role of unmeasured factors such as the need for mechanical ventilation, patient preference for comfort care, bed availability, change in patient condition after admission, and inconsistent application of admission criteria.[17, 21, 32, 33, 34]

After the placement decision, the team could decide if the usual level of monitoring, physician rounding, and care coordination would be adequate for the level of risk or whether an additional anticipatory approach is needed. The weak relationship between the risk of death and incidence of complications, although not a new finding,[35, 36] suggests routine surveillance activities need to be conducted on all patients regardless of risk to detect a complication, but that a rescue plan be developed in advance for high mortality risk patients, for example strata 1 and 2, in the event they should develop a complication.[36] Inclusion of the patient's risk strata as part of the routine hand‐off communication among hospitalists, nurses, and other team members could provide a succinct common alert for the likelihood of adverse events.

The 30‐day mortality risk also informs the transition care plan following hospitalization, given the strong association with death in 180 days and the persistent level of this risk (Figure 3). Again, communication of the risk status (stratum 1) to the team caring for the patient after the hospitalization provides a common reference for prognosis and level of attention needed. However, the prediction accuracy is not sufficient to refer high‐risk patients into hospice, but rather, to identify the high‐risk subset having the most urgent need to have their preferences for future end‐of‐life care understood and addressed. The weak relationship of mortality risk with 30‐day readmissions indicates that our rule would have a limited role in identifying readmission risk per se. Others have noted the difficulty in accurately predicting readmissions, most likely because the underlying causes are multifactorial.[37] Our results suggest that 1 dynamic for readmission is the risk of dying, and so the underlying causes of this risk should be addressed in the transition plan.

There are a number of limitations with our study. First, this rule was developed and validated on data from only 2 institutions, assembled retrospectively, with diagnostic information determined from administrative data. One cannot assume the accuracy will carry over to other institutions[29] or when there is diagnostic uncertainty at the time of admission. Second, the 30‐day mortality risk should not be used as the sole criterion for determining the service intensity for individual patients because of issues with calibration, interpretation of risk, and confounding. The calibration curves (Figure 2) show the slight underprediction of the risk of dying for high‐risk groups. Other studies have also noted problems with precise calibration in validation datasets.[13, 14] Caution is also needed in the interpretation of what it means to be at high risk. Most patients in stratum 1 were alive at 30 days; therefore, being at high risk is not a death sentence. Furthermore, the relative weights of the risk factors reflect (ie, are confounded by) the level of treatment rendered. Some deaths within the higher‐risk percentiles undoubtedly occurred in patients choosing a palliative rather than a curative approach, perhaps partially explaining the slight underprediction of deaths. Conversely, the low mortality experienced by patients within the lower‐risk strata may indicate the treatment provided was effective. Low mortality risk does not imply less care is needed.

A third limitation is that we have not defined the thresholds of risk that should trigger placement and care intensity, although we provide examples on how this could be done. Each institution will need to calibrate the thresholds and associated decision‐making processes according to its own environment.[14] Interested readers can explore the sensitivity and specificity of various thresholds\ by using the tables in the Appendix (see the Supporting information, Appendix II, in the online version of this article). Finally, we do not know if identifying the mortality risk on admission will lead to better outcomes[19, 29]

CONCLUSIONS

Death within 30 days can be predicted with information known at the time of admission, and is associated with the risk of having other adverse events. We believe the probability of death can be used to define strata of risk that provide a succinct common reference point for the multidisciplinary team to anticipate the clinical course of subsets of patients and intervene with proportional intensity.

Favorable health outcomes are more likely to occur when the healthcare team quickly identifies and responds to patients at risk.[1, 2, 3] However, the treatment process can break down during handoffs if the clinical condition and active issues are not well communicated.[4] Patients whose decline cannot be reversed also challenge the health team. Many are referred to hospice late,[5] and some do not receive the type of end‐of‐life care matching their preferences.[6]

Progress toward the elusive goal of more effective and efficient care might be made via an industrial engineering approach, mass customization, in which bundles of services are delivered based on the anticipated needs of subsets of patients.[7, 8] An underlying rationale is the frequent finding that a small proportion of individuals experiences the majority of the events of interest, commonly referenced as the Pareto principle.[7] Clinical prediction rules can help identify these high‐risk subsets.[9] However, as more condition‐specific rules become available, the clinical team faces logistical challenges when attempting to incorporate these into practice. For example, which team member will be responsible for generating the prediction and communicating the level of risk? What actions should follow for a given level of risk? What should be done for patients with conditions not addressed by an existing rule?

In this study, we present our rationale for health systems to implement a process for generating mortality predictions at the time of admission on most, if not all, adult patients as a context for the activities of the various clinical team members. Recent studies demonstrate that in‐hospital or 30‐day mortality can be predicted with substantial accuracy using information available at the time of admission.[10, 11, 12, 13, 14, 15, 16, 17, 18, 19] Relationships are beginning to be explored among the risk factors for mortality and other outcomes such as length of stay, unplanned transfers to intensive care units, 30‐day readmissions, and extended care facility placement.[10, 20, 21, 22] We extend this work by examining how a number of adverse events can be understood through their relationship with the risk of dying. We begin by deriving and validating a new mortality prediction rule using information feasible for our institution to use in its implementation.

METHODS

The prediction rule was derived from data on all inpatients (n = 56,003) 18 to 99 years old from St. Joseph Mercy Hospital, Ann Arbor from 2008 to 2009. This is a community‐based, tertiary‐care center. We reference derivation cases as D1, validation cases from the same hospital in the following year (2010) as V1, and data from a second hospital in 2010 as V2. The V2 hospital belonged to the same parent health corporation and shared some physician specialists with D1 and V1 but had separate medical and nursing staff.

The primary outcome predicted is 30‐day mortality from the time of admission. We chose 30‐day rather than in‐hospital mortality to address concerns of potential confounding of duration of hospital stay and likelihood of dying in the hospital.[23] Risk factors were considered for inclusion into the prediction rule based on their prevalence, conceptual, and univariable association with death (details provided in the Supporting information, Appendix I and II, in the online version of this article). The types of risk factors considered were patient diagnoses as of the time of admission obtained from hospital administrative data and grouped by the 2011 Clinical Classification Software (http://www.hcupus.ahrq.gov/toolssoftware/ccs/ccs.jsp#download, accessed June 6, 2012), administrative data from previous hospitalizations within the health system in the preceding 12 months, and the worst value of clinical laboratory blood tests obtained within 30 days prior to the time of admission. When a given patient had missing values for the laboratory tests of interest, we imputed a normal value, assuming the clinician had not ordered these tests because he/she expected the patient would have normal results. The imputed normal values were derived from available results from patients discharged alive with short hospital stays (3 days) in 2007 to 2008. The datasets were built and analyzed using SAS version 9.1, 9.2 (SAS Institute, Inc., Cary, NC) and R (R Foundation for Statistical Computing, Vienna, Austria; http://www.R‐project.org).

RESULTS

Table 1 displays the risk factors used in the 30‐day mortality prediction rule, their distribution in the populations of interest, and the frequency of the outcomes of interest. The derivation (D1) and validation (V1) populations were clinically similar; the patients of hospital V2 differed in the proportion of risk factors and outcomes. The scoring weights or parameter estimates for the risk factors are given in the Appendix (see Supporting Information, Appendix I, in the online version of this article).

Predicting 30‐Day Mortality

The areas under the ROC (95% confidence interval [CI]) for the D1, V1, and V2 populations were 0.876 (95% CI, 0.870‐0.882), 0.885 (95% CI, 0.877‐0.893), and 0.883 (95% CI, 0.875‐0.892), respectively. The calibration curves for all 3 populations are shown in Figure 1. The overlap of symbols indicates that the level of predicted risk matched actual mortality for most intervals, with slight underprediction for those in the highest risk percentiles.

Figure 1

Example of Risk Strata

Figure 2 displays the relationship between the predicted probability of dying within 30 days and the outcomes of interest for V1, and illustrates the Pareto principle for defining high‐ and low‐risk subgroups. Most of the 30‐day deaths (74.7% of D1, 74.2% of V1, and 85.3% of V2) occurred in the small subset of patients with a predicted probability of death exceeding 0.067 (the top quintile of risk of D1, the top 18 % of V1, and the top 29.8% of V2). In contrast, the mortality rate for those with a predicted risk of 0.0033 was 0.02% for the lowest quintile of risk in D1, 0.07% for the 19.3% having the lowest risk in V1, and 0% for the 9.7% of patients with the lowest risk in V2. Figure 3 indicates that the risk for dying peaks within the first few days of the hospitalization. Moreover, those in the high‐risk group remained at elevated risk relative to the lower risk strata for at least 100 days.

Figure 2

Figure 3

DISCUSSION

The primary contribution of our work concerns the number and strength of associations between the probability of dying within 30 days and other events, and the implications for organizing the healthcare delivery model. We also add to the growing evidence that death within 30 days can be accurately predicted at the time of admission from demographic information, modest levels of diagnostic information, and clinical laboratory values. We developed a new prediction rule with excellent accuracy that compares well to a rule recently developed by the Kaiser Permanente system.[13, 14] Feasibility considerations are likely to be the ultimate determinant of which prediction rule a health system chooses.[13, 14, 29] An independent evaluation of the candidate rules applied to the same data is required to compare their accuracy.

These results suggest a context for the coordination of clinical care processes, although mortality risk is not the only domain health systems must address. For illustrative purposes, we will refer to the risk strata shown in Figure 2. After the decisions to admit the patient to the hospital and whether or not surgical intervention is needed, the next decision concerns the level and type of nursing care needed.[10] Recent studies continue to show challenges both with unplanned transfers to intensive care units[21] and care delivered that is consistently concordant with patient wishes.[6, 30] The level of risk for multiple adverse outcomes suggests stratum 1 patients would be the priority group for perfecting the placement and preference assessment process. Our institution is currently piloting an internal placement guideline recommending that nonpalliative patients in the top 2.5 percentile of mortality risk be placed initially in either an intensive or intermediate care unit to receive the potential benefit of higher nursing staffing levels.[31] However, mortality risk cannot be the only criterion used for placement, as demonstrated by its relatively weak association with overall ICU utilization. Our findings may reflect the role of unmeasured factors such as the need for mechanical ventilation, patient preference for comfort care, bed availability, change in patient condition after admission, and inconsistent application of admission criteria.[17, 21, 32, 33, 34]

After the placement decision, the team could decide if the usual level of monitoring, physician rounding, and care coordination would be adequate for the level of risk or whether an additional anticipatory approach is needed. The weak relationship between the risk of death and incidence of complications, although not a new finding,[35, 36] suggests routine surveillance activities need to be conducted on all patients regardless of risk to detect a complication, but that a rescue plan be developed in advance for high mortality risk patients, for example strata 1 and 2, in the event they should develop a complication.[36] Inclusion of the patient's risk strata as part of the routine hand‐off communication among hospitalists, nurses, and other team members could provide a succinct common alert for the likelihood of adverse events.

The 30‐day mortality risk also informs the transition care plan following hospitalization, given the strong association with death in 180 days and the persistent level of this risk (Figure 3). Again, communication of the risk status (stratum 1) to the team caring for the patient after the hospitalization provides a common reference for prognosis and level of attention needed. However, the prediction accuracy is not sufficient to refer high‐risk patients into hospice, but rather, to identify the high‐risk subset having the most urgent need to have their preferences for future end‐of‐life care understood and addressed. The weak relationship of mortality risk with 30‐day readmissions indicates that our rule would have a limited role in identifying readmission risk per se. Others have noted the difficulty in accurately predicting readmissions, most likely because the underlying causes are multifactorial.[37] Our results suggest that 1 dynamic for readmission is the risk of dying, and so the underlying causes of this risk should be addressed in the transition plan.

There are a number of limitations with our study. First, this rule was developed and validated on data from only 2 institutions, assembled retrospectively, with diagnostic information determined from administrative data. One cannot assume the accuracy will carry over to other institutions[29] or when there is diagnostic uncertainty at the time of admission. Second, the 30‐day mortality risk should not be used as the sole criterion for determining the service intensity for individual patients because of issues with calibration, interpretation of risk, and confounding. The calibration curves (Figure 2) show the slight underprediction of the risk of dying for high‐risk groups. Other studies have also noted problems with precise calibration in validation datasets.[13, 14] Caution is also needed in the interpretation of what it means to be at high risk. Most patients in stratum 1 were alive at 30 days; therefore, being at high risk is not a death sentence. Furthermore, the relative weights of the risk factors reflect (ie, are confounded by) the level of treatment rendered. Some deaths within the higher‐risk percentiles undoubtedly occurred in patients choosing a palliative rather than a curative approach, perhaps partially explaining the slight underprediction of deaths. Conversely, the low mortality experienced by patients within the lower‐risk strata may indicate the treatment provided was effective. Low mortality risk does not imply less care is needed.

A third limitation is that we have not defined the thresholds of risk that should trigger placement and care intensity, although we provide examples on how this could be done. Each institution will need to calibrate the thresholds and associated decision‐making processes according to its own environment.[14] Interested readers can explore the sensitivity and specificity of various thresholds\ by using the tables in the Appendix (see the Supporting information, Appendix II, in the online version of this article). Finally, we do not know if identifying the mortality risk on admission will lead to better outcomes[19, 29]

CONCLUSIONS

Death within 30 days can be predicted with information known at the time of admission, and is associated with the risk of having other adverse events. We believe the probability of death can be used to define strata of risk that provide a succinct common reference point for the multidisciplinary team to anticipate the clinical course of subsets of patients and intervene with proportional intensity.