Original Research

Admission to an intensive care unit (ICU) is lifesaving for some patients, but for many, the admission carries high expectations and financial costs and fails to provide desirable outcomes. Patients who receive intensive care have a mortality rate of about 20%, and the costs of this care comprise about 4% of the U.S. health care budget.^1,2 In a study of Medicare recipients, treatment intensity and expenses increased between the mid-1980s and 1999 but without any increase in survivorship; per capita ICU expenses were higher for patients who did not survive the ICU.³ Use of the ICU in patients’ final stages of life has increased in proportion since then, and the demand for critical care is likely to continue as the relative proportion of elderly patients in the population rises.^2,4,5

Physicians and nurses who responded to a European survey on the inappropriateness of intensive care overwhelmingly endorsed the problems of “too much care” (89%) and “other patients would benefit more” (38%).⁶ Receiving terminal care in the ICU runs counter to the preferences of most patients.⁷ Therefore, the challenges are to define the true beneficiaries of critical care and to minimize the discomfort and unrealistic expectations of patients who will not benefit from intensive care.

For ICU patients, morbidity and mortality depend on the interaction of an acute insult (or a surgery), major comorbidities, and physiologic reserve. Aside from those with objective criteria of extreme illness, many patients have an indeterminate prognosis that is difficult to reliably predict.^8,9 Several prognostic scores, including the APACHE (Acute Physiologic Assessment and Chronic Health Evaluation) and SOFA (Sequential Organ Failure Assessment) scores, have proved useful in understanding the illness burden of a population when comparing outcomes in different ICUs. Yet their use in assessing the survival of individual patients has not been advocated.^10-15 The utility of such models is further challenged by the significant differences in survival between patients with similar illness scores; by the sometimes poor applicability of a model’s derivation cohort to other ICU populations (surgical in particular); by cases of huge disparities between actual and predicted mortality; and by the periodic need to recalibrate models according to advances in care.^16-20

 

Physician intuition regarding prognosis is highly variable. In a series of medical (floor and ICU) admissions, resident physician estimates of illness severity and postdischarge status were associated with stepwise differences in mortality and APACHE scores.^21,22 However, in a pure ICU population, in most cases seasoned providers could not accurately predict a patient’s chance of survival.²³ Physicians are likewise poor in predicting family preferences regarding aggressive care vs alternatives, and often, survival is couched in terms of ICU survival, which for family members may not be as meaningful as long-term survival or functional recovery. Further, quality of life and patient preferences are not discussed in most cases, even those associated with poor outcomes.²⁴ There also is a large amount of heterogeneity in the end of-life care of ICU patients. For example, cardiopulmonary resuscitation was attempted in up to 70% of dying patients in some ICUs and in as little as 4% in other ICUs.²⁵ Thus, the limitations of predictive models, combined with misperceptions of patient preference, poor communication, and local traditions, lead to aggressive care being given to patients who might not benefit from or desire such care.

It has been stated that the trajectory of most critical illness is unclear enough so that patients should be admitted to the ICU for a trial of therapy, and that in outcome predictions, the response to intensive treatment may be more useful than laboratory and other data comprising illness severity scores.^15,26 However, there is no consensus as to what constitutes a trial of intensive care therapy—vs a round of chemotherapy, a course of antibiotics, or a palliative ileostomy—yet this is the basis of many ICU admissions. Slight corrections in laboratory or physiologic findings often lead to continuation of aggressive care, often without any discussion of expected outcomes and the process of identifying and caring for patients who do not respond to therapy. Intensive care also may be prolonged because of several medical, personal, and social factors (Table 1).

At best, deciding how long to provide intensive care involves a synthesis of information about the trajectory, physiologic reserve, beliefs, values, and preferences of the patient. Any or all of these elements may not be known to the care decision-makers.

The authors conducted a study to determine whether a particular duration of care exists that represents a reasonable trial of therapy. As the VA Palo Alto Health Care System (VAPAHCS) ICU treats both medical and surgical patients, the authors were able to compare these subpopulations’ outcomes while providing the same standard of care. They analyzed the aggregate of patients as well as the medical and surgical subpopulations.

 

Methods

The VA Research and Development Committee and the Stanford Panel on Human Subjects approved the authors’ data collection and reporting.The study was conducted at the 15-bed mixed-medical/surgical VAPAHCS ICU. Analyzed data were drawn from all patients admitted during a 19-month period (July 14, 2008, to January 28, 2010). A serial log was used to prospectively capture basic data regarding each admission. Medical patients received care from the ICU service, and surgical patients were comanaged by the surgical and ICU teams.

A mortality database was constructed with data from the Decedent Affairs Office and from the national VistA database. The data included all deaths recorded either inside or outside the hospital or systemwide nursing facility. Mortality reported in the Computerized Patient Record System (CPRS) was queried further for patients with a length of stay (LOS) of more than 14 days.

Statistical Analysis

Calculations were based on denominators of individual patients or on number of admissions. All mortality calculations were based on a denominator of individual patients. For mortality analysis, only the last admission was included, unless a patient survived a full year between admissions. The Kruskal-Wallis test for nonnormally distributed data and the Dunn posttest for multiple comparisons were used for continuous variables (eg, age, LOS, risk scores); the Fisher exact test was used for categorical data; and the log-rank test was used to compare survival curves. For all analyses, P < .05 was considered statistically significant.

Mortality and Functional Status

Mortality risk scores on ICU admission were calculated with the Mortality Prediction Model–Admission III (MPM-III), using data from the CPRS. Specifics on this calculation are described in the eAppendix.

Current survival status of patients who were in the ICU more than 14 days was determined from the CPRS and telephone discussions with the patient or with relatives. Functional status was evaluated with the 36-Item Short Form Health Survey (SF-36), which has been used in comparable studies.^27,28 Disposition at 6 months and 1 year was established by inspecting the CPRS for dates corresponding to these exact periods. For example, a patient in the hospital about 1 year after ICU discharge would be considered to be at home if discharged 1 day before the 365-day anniversary. In a few cases, progress notes indicated that the patient was receiving around-the-clock nursing care at home; in the analysis, these cases were included with those of patients known to be in traditional nursing facilities. In cases in which the CPRS lacked mortality information, the patient was presumed to be alive even if there were no records of clinic visits or other medical attention. Serial admission data from a mixed-medical/surgical ICU were collected over a 19-month period (July 14, 2008, to January 28, 2010) and analyzed.

Results

The final data set consisted of 1,113 admissions and 976 patients (one-third medical, two-thirds surgical). In this cohort, 12% of all patients studied were readmitted to the ICU at least once, and 12% of all ICU admissions were repeat admissions. The medical/surgical proportion was similar for readmitted patients. Demographics and other data are available in eTable 1.

Length of Stay

The distribution of all patients by LOS in the study period is shown in eFigure 1A. Data are skewed rightward toward longer LOS. The median LOS of 3 days for the entire population differed according to specialty, with a median of 3 days for medical patients (interquartile range, 2-7 days) and a median of 2 days for surgical patients (interquartile range, 1-5 days; P < .01 for medical vs surgical patients).

The LOS differed between ICU patients admitted for the first time and those readmitted within the 19-month study period. For both admission categories, LOS was longer for medical patients than for surgical patients. However, there were no significant differences between percentages of medical and surgical patients who were readmitted (Table 2). Despite comprising about 12% of the population, patients with more than 1 admission accounted for 23% of admissions and 25% of all bed occupancies during the study period.

Figure e1B shows ICU bed occupancy for different LOS intervals (calculated as bed days) and indicates that despite accounting for a small percentage of admissions, patients with long LOS accounted for a significant portion of total occupancy (32% for more than 1 month, 45% for more than 14 days). The medical and surgical contributions of these long-LOS patients were about equal. The figures indicate that more than half of medical ICU patient occupancy involved LOS of more than 14 days, while surgical patients tended to have shorter LOS.

 

Mortality

Of all the patients in this study, 5.1% died in the ICU; the mortality rate was 11% for medical patients and 2.1% for surgical patients. Thirty days after discharge, overall mortality was 10.4%, or 23.5% for medical patients and 3.9% for surgical patients. Finally, 1 year after discharge, mortality rates were 21.5% (overall), 39.4% (medical patients), and 12.5% (surgical patients) (Table 3). Survival curves demonstrated the difference between medical and surgical patients at 30 days and 1 year (Figures 1A & 1B).

 

Impact of LOS on Mortality

One-year mortality was 17% for patients who were in the ICU less than 14 days and 40% for those in the ICU more than 14 days (relative risk [RR] = 2.35; P < .01) (Table 4).

In the under-14-days group, mortality was significantly higher (RR = 3.3; P < .0001) for medical patients (33%) than for surgical patients (10%). A significant association between LOS and mortality was found for admissions of 0 to 7 days (r² = 0.63; P < .05) and up to 6 weeks (r² = 0.88; P < .01) (Figures 2A & 2B). At each LOS, mortality was significantly higher for medical patients than for surgical patients.

Survival curves of both medical and surgical patients with LOS of 0 to 7 days, 8 to 14 days, and more than 14 days showed a similar significantly higher mortality rate associated with longer ICU duration (Figures 3A & 3B).

Mortality also was higher in patients with more than 1 ICU admission. For the aggregate of ICU patients, readmission status was significantly associated with a 10% increase in mortality. For both single- and multiple-admission status, the mortality rate was 2.5-fold higher for medical patients than for surgical patients. The increased mortality associated with readmission status was not significantly different for either medical or surgical patients analyzed as subgroups (eAppendix Table.)

Impact of Age on Mortality

Figures 4A and 4B shows 30-day and 1-year mortality associated with age; regression analysis indicated that age is an independent predictor of ICU mortality. For 30-day mortality, increased age was positively associated with mortality in medical patients but not in surgical patients (r² = 0.91; P < .0001). Age had a significant impact on 1-year mortality for both medical and surgical patients but less so in the latter (r² = 0.95 and 0.65, respectively; P < .001 for both). Although increased mortality was associated with both LOS and age, there was no clear association between the latter 2 variables.

Survival of Chronic Critical Illness

As eTable 2 shows, 21.5% of all patients died either in the ICU or within the first year after ICU discharge. To evaluate the survival of chronic ICU residence, the authors performed a detailed analysis of functional status and mortality of patients with LOS of more than 14 days. Seventy-one patients fit that profile (their mean LOS was 41 days; median, 28 days). Of these patients, 11 died in the ICU, and another 17 died within 6 months (including 2 in a stepdown unit and 7 in hospice). Overall, 28 (39%) of the 71 patients died either in the ICU or within 6 months (35% aggregate, 53% of medical patients, and 27% of surgical patients in ICU > 2 weeks). Another 8 patients (11%) died between 6 and 12 months after discharge. One-year mortality among patients in the ICU more than 14 days was 40% overall, 50% for medical patients, and 29% for surgical patients—or twice that predicted by the MPM-III model, which figured mortality rates of 25% and 12% for medical and surgical patients, respectively. In this cohort, the mean MPM-III score was 18.6% for 1-year survivors and 29.3% for nonsurvivors (P = .016, Mann-Whitney U test). Mortality was associated with a trend toward higher MPM-III scores in both medical and surgical patients but did not reach statistical significance.

Of the cohort patients who lived at least 6 months after ICU discharge, 45% were still in a hospital or were in a nursing facility at 6 months. Of the patients who lived at least 1 year, 33% were still in a hospital or were in a nursing facility (Figure 5). At 1 year, mean age was 63 years for survivors and 69 years for the deceased (P < .01 by Student t test).

There were no significant associations among age, LOS, and nursing facility residence at 1 year. Compared with surgical patients, a larger percentage of medical patients required skilled nursing at 1 year (RR = 1.95; P = .042 by Fisher exact test).

 

Quality-of-Life Survey

The authors successfully contacted 32 of the 39 patients who lived at least 1 year after discharge after an ICU stay of more than 14 days. The subgroups’ median SF-36 scores were similar: 57 for medical patients and 51 for surgical patients. These average scores over 8 domains are similar to those reported by Graf and colleagues for 9 months after ICU discharge (53.7) and are lower than the normative data reported by those authors for the German population (mean, 66.5).²⁹

Discussion

The goals of the present study were 2-fold—to gain a better understanding of the survival and functioning of patients after ICU residence and to define what may constitute a trial of therapy in ICU, or specifically to determine whether there is a particular ICU interval or point at which further care fails to improve survival. The study also compared medical and surgical subpopulations.

The main finding of this study was a 4-fold difference between ICU mortality and 1-year mortality. This mortality increase occurred in both medical and surgical patients, but there were large differences in magnitude between these groups. The survival rates generally were better than those of other general intensive care populations, though such a comparison should be made with caution, as survival differs by country, population, admitting practices, and a variety of other hospital characteristics.^30,31 Although some findings of the present study may relate to its largely male U.S. veteran population, the authors believe they have provided a data-collection-and-analysis model that can be used by any hospital trying to understand the course and outcome of its ICU patients and recognizing the value of this knowledge in discussions on goals of care.

Mortality and LOS

As each interval of ICU residence was associated with a stepwise increase in mortality, there was no clear cutoff for diminishing return. To create a reference point, the authors analyzed the data of patients who were in an ICU more than 14 days—thinking that this duration may represent an outer limit of a reasonable trial of therapy and a measure that probably distinguishes acute from chronic critical illness.³² Use of this interval represented a conservative approach, as only 6.5% of the patients in this cohort had a LOS of more than 14 days. This small percentage of patients accounted for 45% of total bed occupancy in this study and 54% of all medical bed occupancy. In the more-than-14-days group, mortality was 37.5% for surgical patients and 46.3% for medical patients. Thus, LOS may be a dynamic measure of physiologic reserve and disease severity—reflecting variables such as response to therapy, severity of comorbidities, resistance to new problems, and rebound from chronic stress, inflammation, and catabolism. This view is supported by the nearly 2-fold higher mortality in medical patients and nearly 3-fold higher mortality in surgical patients in comparison with MPM-III predictions.

Twelve percent of all patients were admitted to ICU multiple times, and these admissions accounted for 25% of all bed occupancies. Multiple admissions indicate a high disease burden or a low physiologic reserve that prevents full recovery from critical illness. As mortality was higher in patients with multiple admissions, ICU readmission should be regarded as a marker for poor overall recovery and should prompt consideration of both initial discharge criteria and trajectory as well as goals of care.

Medical vs Surgical Patients

In this cohort, medical and surgical patients were distinguished on several grounds. Despite the similar mean age of these subpopulations, medical patients had longer LOS and higher short- and long-term mortality. These findings are not surprising, as medical patients in the ICU have high rates of end-stage disease, malignancy, and high comorbidity burden and are often admitted to have potentially life-ending conditions stabilized. Surgical patients generally are selected on their ability to withstand major systemic perturbations—palliative and emergency operations excepted—and generally have medical conditions optimized before surgery. As the expectation of postoperative survival likely biases clinician behavior toward aggressive care, some short-term survival may reflect this behavior.

In contrast, such biased behavior is not an issue in 1-year survival, which instead accurately reflects underlying health. The different slopes of medical and surgical patients on age-vs-mortality in Figures 4A & 4B indicate the different physiologic makeups of these ICU patients. With short and long LOS compared, the difference between surgical and medical patients in the ICU is striking: Sixty-one percent of all surgical bed days vs 45% of all medical bed days are for LOS less than 14 days. Nevertheless, chronic critical illness has a significant impact on both medical and surgical patients and tends to equalize some of the survival differences between these groups. These populations had similar ICU readmission rates as well as similar higher mortality rates for LOS of more than 14 days and especially for LOS of more than 1 month. With longer LOS, the survival curve of surgical patients begins to resemble that of medical patients—suggesting that the phenotype of chronic critical illness becomes the dominant force influencing survival and function (Figures 3A & 3B). Indeed, for surgical patients, the highest mortality categories were ICU readmission and LOS of more than 30 days.

 

The mortality rate was significantly lower for surgical patients than it was for medical patients at all intervals studied, with the largest separation in the short-term categories of ICU and 30-day mortality. The post-ICU mortality rates for medical and surgical patients are similar to those reported in several other studies, including a study of veterans.^14-16,33,34 Among the present patients with LOS of more than 14 days, surviving surgical patients were significantly younger than nonsurviving surgical patients and both surviving and nonsurviving medical patients.

  

The few SF-36 responses collected revealed no differences between medical and surgical patients.

A Trial of Therapy

The present data are useful in describing the landscape of post-ICU survival to patients and their families. The data demonstrated a higher mortality trend that correlated with increases in age and increases in ICU duration and readmission. Within this continuum, there was no break point at which survivors and nonsurvivors clearly separated. The data therefore lack a boundary that can be used to define a trial of therapy. However, the added risks of age and recovery longer than 1 week are clear and should be included in care decisions. The generally better survival of surgical patients (nearly all of whom had elective surgery) in comparison with medical patients suggests these populations should be considered separately.

In the absence of a point distinguishing survivors from nonsurvivors, the authors performed a more detailed analysis of patients in the ICU for more than 14 days to provide some perspective on health care dependence in the subsequent year. That ICU survival does not necessarily equate to overall survival and independence long after ICU residence is an important matter for patients and families to consider when making decisions about critical care residence. The 14-day LOS data, though using a fairly arbitrary time point, suggest that patients who cannot recover from critical illness in less than 14 days should be advised of the range of short- and long-term mortality and the likelihood of high dependence on medical care within the subsequent year.

The concepts of hospital-dependent patient and persistent inflammation, immunosuppression, and catabolism syndrome have been introduced to describe the condition of progressive deterioration and inability to regain full independence after illness.^32,35 These illness patterns deserve attention in prognosis discussions. The present study focused not on ICU survival but on 1-year mortality and functional independence, and it is these longer term outcomes that critical care professionals should consider. Intensive care units are successful in improving short-term survival, but a long line of successful ICU discharges may lead an intensivist to think that longer term survival is important as well and convey this impression to patients and their families.

Study Strengths

This study is one of a few to investigate the short- and long-term survival of an unselected cohort of critically ill patients and is unique in its inclusion of both medical and surgical patients receiving care in the same environment. Medical and surgical patients have different survival profiles that may necessitate separate studies of these subpopulations. However, the finding of different survival profiles under the same care highlights the intrinsic differences between these groups. Use of a 1.5-year study period allowed the authors to capture ICU patients with long LOS and to include multiple episodes of care provided by more than 10 different attending physicians. Therefore, these data likely were not influenced by any rare events or idiosyncrasies in practice styles. Further, the same teams of physicians and nurses cared for all the medical and surgical patients, and all unit-based protocols and quality improvement activities were applied to all patients.

Study Limitations

The intensive care patients come from a large catchment area; however, conditions seen in tertiary referral centers, such as bone marrow transplants, cerebrovascular, transplant surgery, and ventricular-assist devices are not represented in this population.

In this study, bed days were used as a crude measure of care burden. From a nursing perspective, however, the workload may be higher with quick-turnover beds than with long-term residents. On the other hand, long-term ICU residents are visited by multiple consultants and receive a much larger set of interventions, including weeks of ventilation and hemodialysis, line changes, and family meetings. A comparison of the costs involved for different ICU subpopulations would add valuable information to this discussion.

The authors took a conservative approach in establishing the mortality and residence of patients 1 year after their ICU stays. At 6 months, 1-year patients without evidence of hospital or nursing facility residence were assumed to be home. In reality, nearly all these patients had multiple admissions or emergency department stays, or there was other evidence of intensive care. Some patients who were assumed to be home may have left the area and become untraceable. All estimates of care dependence and mortality should therefore be considered minimums. The authors cannot envision how any of their estimates could overstate the morbidity and mortality.

 

The concept of hospital dependence is applicable to the majority of the ICU survivors, though the authors did not attempt to create a quantitative measure of this status.³⁶ Another study limitation is that absence of hospitalization does not equal functional independence. A better definition of this status, and its application to a broad spectrum of LOS, would be a valuable adjunct to ICU decision making.

The convention by which the authors considered the first day of their study period a “fresh slate” did not adjust for the situation that some first admissions actually were readmissions. Assuming the validity of the finding that readmitted patients had a higher burden of morbidity and mortality, misclassification of admission status would tend to inflate the mortality of single-admission patients and minimize the magnitude of the differences found in this study. Similarly, an admission near the end of the study period may have been analyzed as a single admission, even if the patient was readmitted and died the next year. The latter situation also would tend to inflate the mortality of the single-admission category. None of these possible mathematical errors negates the fact that a second ICU admission should be regarded as a marker for poor recovery.

A more accurate estimate of short- and long-term prognosis likely can be obtained by examining laboratory studies and interventions such as vasopressors, dialysis, and ventilation at defined time points. Although the authors did not attempt it, development of such a model would be a valuable undertaking. They focused on describing the expected course of ICU patients and determining what patterns emerged from care duration. As this study found that the prognosis for long-term ICU residents remained guarded a long time after discharge, survival models of patients with 1- to 2-week ICU residences likely would be valuable in clinical decision making.

A quality-of-life survey was administered only to patients in the ICU longer than 2 weeks. This limited study was conducted to explore the feasibility of assessing outcomes other than survival and to determine the staffing requirements needed to research this further. A more meaningful analysis would come from a broader analysis of scores from 3 or 4 different ICU lengths of stay.

Clinician and family behavior can influence some of the outcomes measured in this study—particularly in cases in which an illness is poorly characterized and an evidence basis for decision making is lacking. In these situations, values and individual clinician judgment likely predominate, possibly introducing variability to care duration. Nevertheless, cumulative mortality 1 month or more after ICU residence would eliminate biased clinician behavior. The heterogeneity of care providers’ and families’ decision making, captured in this analysis, likely is a normal phenomenon that should help inform physicians’ understanding of prolonged ICU residence. 

Admission to an intensive care unit (ICU) is lifesaving for some patients, but for many, the admission carries high expectations and financial costs and fails to provide desirable outcomes. Patients who receive intensive care have a mortality rate of about 20%, and the costs of this care comprise about 4% of the U.S. health care budget.^1,2 In a study of Medicare recipients, treatment intensity and expenses increased between the mid-1980s and 1999 but without any increase in survivorship; per capita ICU expenses were higher for patients who did not survive the ICU.³ Use of the ICU in patients’ final stages of life has increased in proportion since then, and the demand for critical care is likely to continue as the relative proportion of elderly patients in the population rises.^2,4,5

Physicians and nurses who responded to a European survey on the inappropriateness of intensive care overwhelmingly endorsed the problems of “too much care” (89%) and “other patients would benefit more” (38%).⁶ Receiving terminal care in the ICU runs counter to the preferences of most patients.⁷ Therefore, the challenges are to define the true beneficiaries of critical care and to minimize the discomfort and unrealistic expectations of patients who will not benefit from intensive care.

For ICU patients, morbidity and mortality depend on the interaction of an acute insult (or a surgery), major comorbidities, and physiologic reserve. Aside from those with objective criteria of extreme illness, many patients have an indeterminate prognosis that is difficult to reliably predict.^8,9 Several prognostic scores, including the APACHE (Acute Physiologic Assessment and Chronic Health Evaluation) and SOFA (Sequential Organ Failure Assessment) scores, have proved useful in understanding the illness burden of a population when comparing outcomes in different ICUs. Yet their use in assessing the survival of individual patients has not been advocated.^10-15 The utility of such models is further challenged by the significant differences in survival between patients with similar illness scores; by the sometimes poor applicability of a model’s derivation cohort to other ICU populations (surgical in particular); by cases of huge disparities between actual and predicted mortality; and by the periodic need to recalibrate models according to advances in care.^16-20

 

Physician intuition regarding prognosis is highly variable. In a series of medical (floor and ICU) admissions, resident physician estimates of illness severity and postdischarge status were associated with stepwise differences in mortality and APACHE scores.^21,22 However, in a pure ICU population, in most cases seasoned providers could not accurately predict a patient’s chance of survival.²³ Physicians are likewise poor in predicting family preferences regarding aggressive care vs alternatives, and often, survival is couched in terms of ICU survival, which for family members may not be as meaningful as long-term survival or functional recovery. Further, quality of life and patient preferences are not discussed in most cases, even those associated with poor outcomes.²⁴ There also is a large amount of heterogeneity in the end of-life care of ICU patients. For example, cardiopulmonary resuscitation was attempted in up to 70% of dying patients in some ICUs and in as little as 4% in other ICUs.²⁵ Thus, the limitations of predictive models, combined with misperceptions of patient preference, poor communication, and local traditions, lead to aggressive care being given to patients who might not benefit from or desire such care.

It has been stated that the trajectory of most critical illness is unclear enough so that patients should be admitted to the ICU for a trial of therapy, and that in outcome predictions, the response to intensive treatment may be more useful than laboratory and other data comprising illness severity scores.^15,26 However, there is no consensus as to what constitutes a trial of intensive care therapy—vs a round of chemotherapy, a course of antibiotics, or a palliative ileostomy—yet this is the basis of many ICU admissions. Slight corrections in laboratory or physiologic findings often lead to continuation of aggressive care, often without any discussion of expected outcomes and the process of identifying and caring for patients who do not respond to therapy. Intensive care also may be prolonged because of several medical, personal, and social factors (Table 1).

At best, deciding how long to provide intensive care involves a synthesis of information about the trajectory, physiologic reserve, beliefs, values, and preferences of the patient. Any or all of these elements may not be known to the care decision-makers.

The authors conducted a study to determine whether a particular duration of care exists that represents a reasonable trial of therapy. As the VA Palo Alto Health Care System (VAPAHCS) ICU treats both medical and surgical patients, the authors were able to compare these subpopulations’ outcomes while providing the same standard of care. They analyzed the aggregate of patients as well as the medical and surgical subpopulations.

 

Methods

The VA Research and Development Committee and the Stanford Panel on Human Subjects approved the authors’ data collection and reporting.The study was conducted at the 15-bed mixed-medical/surgical VAPAHCS ICU. Analyzed data were drawn from all patients admitted during a 19-month period (July 14, 2008, to January 28, 2010). A serial log was used to prospectively capture basic data regarding each admission. Medical patients received care from the ICU service, and surgical patients were comanaged by the surgical and ICU teams.

A mortality database was constructed with data from the Decedent Affairs Office and from the national VistA database. The data included all deaths recorded either inside or outside the hospital or systemwide nursing facility. Mortality reported in the Computerized Patient Record System (CPRS) was queried further for patients with a length of stay (LOS) of more than 14 days.

Statistical Analysis

Calculations were based on denominators of individual patients or on number of admissions. All mortality calculations were based on a denominator of individual patients. For mortality analysis, only the last admission was included, unless a patient survived a full year between admissions. The Kruskal-Wallis test for nonnormally distributed data and the Dunn posttest for multiple comparisons were used for continuous variables (eg, age, LOS, risk scores); the Fisher exact test was used for categorical data; and the log-rank test was used to compare survival curves. For all analyses, P < .05 was considered statistically significant.

Mortality and Functional Status

Mortality risk scores on ICU admission were calculated with the Mortality Prediction Model–Admission III (MPM-III), using data from the CPRS. Specifics on this calculation are described in the eAppendix.

Current survival status of patients who were in the ICU more than 14 days was determined from the CPRS and telephone discussions with the patient or with relatives. Functional status was evaluated with the 36-Item Short Form Health Survey (SF-36), which has been used in comparable studies.^27,28 Disposition at 6 months and 1 year was established by inspecting the CPRS for dates corresponding to these exact periods. For example, a patient in the hospital about 1 year after ICU discharge would be considered to be at home if discharged 1 day before the 365-day anniversary. In a few cases, progress notes indicated that the patient was receiving around-the-clock nursing care at home; in the analysis, these cases were included with those of patients known to be in traditional nursing facilities. In cases in which the CPRS lacked mortality information, the patient was presumed to be alive even if there were no records of clinic visits or other medical attention. Serial admission data from a mixed-medical/surgical ICU were collected over a 19-month period (July 14, 2008, to January 28, 2010) and analyzed.

Results

The final data set consisted of 1,113 admissions and 976 patients (one-third medical, two-thirds surgical). In this cohort, 12% of all patients studied were readmitted to the ICU at least once, and 12% of all ICU admissions were repeat admissions. The medical/surgical proportion was similar for readmitted patients. Demographics and other data are available in eTable 1.

Length of Stay

The distribution of all patients by LOS in the study period is shown in eFigure 1A. Data are skewed rightward toward longer LOS. The median LOS of 3 days for the entire population differed according to specialty, with a median of 3 days for medical patients (interquartile range, 2-7 days) and a median of 2 days for surgical patients (interquartile range, 1-5 days; P < .01 for medical vs surgical patients).

The LOS differed between ICU patients admitted for the first time and those readmitted within the 19-month study period. For both admission categories, LOS was longer for medical patients than for surgical patients. However, there were no significant differences between percentages of medical and surgical patients who were readmitted (Table 2). Despite comprising about 12% of the population, patients with more than 1 admission accounted for 23% of admissions and 25% of all bed occupancies during the study period.

Figure e1B shows ICU bed occupancy for different LOS intervals (calculated as bed days) and indicates that despite accounting for a small percentage of admissions, patients with long LOS accounted for a significant portion of total occupancy (32% for more than 1 month, 45% for more than 14 days). The medical and surgical contributions of these long-LOS patients were about equal. The figures indicate that more than half of medical ICU patient occupancy involved LOS of more than 14 days, while surgical patients tended to have shorter LOS.

 

Mortality

Of all the patients in this study, 5.1% died in the ICU; the mortality rate was 11% for medical patients and 2.1% for surgical patients. Thirty days after discharge, overall mortality was 10.4%, or 23.5% for medical patients and 3.9% for surgical patients. Finally, 1 year after discharge, mortality rates were 21.5% (overall), 39.4% (medical patients), and 12.5% (surgical patients) (Table 3). Survival curves demonstrated the difference between medical and surgical patients at 30 days and 1 year (Figures 1A & 1B).

 

Impact of LOS on Mortality

One-year mortality was 17% for patients who were in the ICU less than 14 days and 40% for those in the ICU more than 14 days (relative risk [RR] = 2.35; P < .01) (Table 4).

In the under-14-days group, mortality was significantly higher (RR = 3.3; P < .0001) for medical patients (33%) than for surgical patients (10%). A significant association between LOS and mortality was found for admissions of 0 to 7 days (r² = 0.63; P < .05) and up to 6 weeks (r² = 0.88; P < .01) (Figures 2A & 2B). At each LOS, mortality was significantly higher for medical patients than for surgical patients.

Survival curves of both medical and surgical patients with LOS of 0 to 7 days, 8 to 14 days, and more than 14 days showed a similar significantly higher mortality rate associated with longer ICU duration (Figures 3A & 3B).

Mortality also was higher in patients with more than 1 ICU admission. For the aggregate of ICU patients, readmission status was significantly associated with a 10% increase in mortality. For both single- and multiple-admission status, the mortality rate was 2.5-fold higher for medical patients than for surgical patients. The increased mortality associated with readmission status was not significantly different for either medical or surgical patients analyzed as subgroups (eAppendix Table.)

Impact of Age on Mortality

Figures 4A and 4B shows 30-day and 1-year mortality associated with age; regression analysis indicated that age is an independent predictor of ICU mortality. For 30-day mortality, increased age was positively associated with mortality in medical patients but not in surgical patients (r² = 0.91; P < .0001). Age had a significant impact on 1-year mortality for both medical and surgical patients but less so in the latter (r² = 0.95 and 0.65, respectively; P < .001 for both). Although increased mortality was associated with both LOS and age, there was no clear association between the latter 2 variables.

Survival of Chronic Critical Illness

As eTable 2 shows, 21.5% of all patients died either in the ICU or within the first year after ICU discharge. To evaluate the survival of chronic ICU residence, the authors performed a detailed analysis of functional status and mortality of patients with LOS of more than 14 days. Seventy-one patients fit that profile (their mean LOS was 41 days; median, 28 days). Of these patients, 11 died in the ICU, and another 17 died within 6 months (including 2 in a stepdown unit and 7 in hospice). Overall, 28 (39%) of the 71 patients died either in the ICU or within 6 months (35% aggregate, 53% of medical patients, and 27% of surgical patients in ICU > 2 weeks). Another 8 patients (11%) died between 6 and 12 months after discharge. One-year mortality among patients in the ICU more than 14 days was 40% overall, 50% for medical patients, and 29% for surgical patients—or twice that predicted by the MPM-III model, which figured mortality rates of 25% and 12% for medical and surgical patients, respectively. In this cohort, the mean MPM-III score was 18.6% for 1-year survivors and 29.3% for nonsurvivors (P = .016, Mann-Whitney U test). Mortality was associated with a trend toward higher MPM-III scores in both medical and surgical patients but did not reach statistical significance.

Of the cohort patients who lived at least 6 months after ICU discharge, 45% were still in a hospital or were in a nursing facility at 6 months. Of the patients who lived at least 1 year, 33% were still in a hospital or were in a nursing facility (Figure 5). At 1 year, mean age was 63 years for survivors and 69 years for the deceased (P < .01 by Student t test).

There were no significant associations among age, LOS, and nursing facility residence at 1 year. Compared with surgical patients, a larger percentage of medical patients required skilled nursing at 1 year (RR = 1.95; P = .042 by Fisher exact test).

 

Quality-of-Life Survey

The authors successfully contacted 32 of the 39 patients who lived at least 1 year after discharge after an ICU stay of more than 14 days. The subgroups’ median SF-36 scores were similar: 57 for medical patients and 51 for surgical patients. These average scores over 8 domains are similar to those reported by Graf and colleagues for 9 months after ICU discharge (53.7) and are lower than the normative data reported by those authors for the German population (mean, 66.5).²⁹

Discussion

The goals of the present study were 2-fold—to gain a better understanding of the survival and functioning of patients after ICU residence and to define what may constitute a trial of therapy in ICU, or specifically to determine whether there is a particular ICU interval or point at which further care fails to improve survival. The study also compared medical and surgical subpopulations.

The main finding of this study was a 4-fold difference between ICU mortality and 1-year mortality. This mortality increase occurred in both medical and surgical patients, but there were large differences in magnitude between these groups. The survival rates generally were better than those of other general intensive care populations, though such a comparison should be made with caution, as survival differs by country, population, admitting practices, and a variety of other hospital characteristics.^30,31 Although some findings of the present study may relate to its largely male U.S. veteran population, the authors believe they have provided a data-collection-and-analysis model that can be used by any hospital trying to understand the course and outcome of its ICU patients and recognizing the value of this knowledge in discussions on goals of care.

Mortality and LOS

As each interval of ICU residence was associated with a stepwise increase in mortality, there was no clear cutoff for diminishing return. To create a reference point, the authors analyzed the data of patients who were in an ICU more than 14 days—thinking that this duration may represent an outer limit of a reasonable trial of therapy and a measure that probably distinguishes acute from chronic critical illness.³² Use of this interval represented a conservative approach, as only 6.5% of the patients in this cohort had a LOS of more than 14 days. This small percentage of patients accounted for 45% of total bed occupancy in this study and 54% of all medical bed occupancy. In the more-than-14-days group, mortality was 37.5% for surgical patients and 46.3% for medical patients. Thus, LOS may be a dynamic measure of physiologic reserve and disease severity—reflecting variables such as response to therapy, severity of comorbidities, resistance to new problems, and rebound from chronic stress, inflammation, and catabolism. This view is supported by the nearly 2-fold higher mortality in medical patients and nearly 3-fold higher mortality in surgical patients in comparison with MPM-III predictions.

Twelve percent of all patients were admitted to ICU multiple times, and these admissions accounted for 25% of all bed occupancies. Multiple admissions indicate a high disease burden or a low physiologic reserve that prevents full recovery from critical illness. As mortality was higher in patients with multiple admissions, ICU readmission should be regarded as a marker for poor overall recovery and should prompt consideration of both initial discharge criteria and trajectory as well as goals of care.

Medical vs Surgical Patients

In this cohort, medical and surgical patients were distinguished on several grounds. Despite the similar mean age of these subpopulations, medical patients had longer LOS and higher short- and long-term mortality. These findings are not surprising, as medical patients in the ICU have high rates of end-stage disease, malignancy, and high comorbidity burden and are often admitted to have potentially life-ending conditions stabilized. Surgical patients generally are selected on their ability to withstand major systemic perturbations—palliative and emergency operations excepted—and generally have medical conditions optimized before surgery. As the expectation of postoperative survival likely biases clinician behavior toward aggressive care, some short-term survival may reflect this behavior.

In contrast, such biased behavior is not an issue in 1-year survival, which instead accurately reflects underlying health. The different slopes of medical and surgical patients on age-vs-mortality in Figures 4A & 4B indicate the different physiologic makeups of these ICU patients. With short and long LOS compared, the difference between surgical and medical patients in the ICU is striking: Sixty-one percent of all surgical bed days vs 45% of all medical bed days are for LOS less than 14 days. Nevertheless, chronic critical illness has a significant impact on both medical and surgical patients and tends to equalize some of the survival differences between these groups. These populations had similar ICU readmission rates as well as similar higher mortality rates for LOS of more than 14 days and especially for LOS of more than 1 month. With longer LOS, the survival curve of surgical patients begins to resemble that of medical patients—suggesting that the phenotype of chronic critical illness becomes the dominant force influencing survival and function (Figures 3A & 3B). Indeed, for surgical patients, the highest mortality categories were ICU readmission and LOS of more than 30 days.

 

The mortality rate was significantly lower for surgical patients than it was for medical patients at all intervals studied, with the largest separation in the short-term categories of ICU and 30-day mortality. The post-ICU mortality rates for medical and surgical patients are similar to those reported in several other studies, including a study of veterans.^14-16,33,34 Among the present patients with LOS of more than 14 days, surviving surgical patients were significantly younger than nonsurviving surgical patients and both surviving and nonsurviving medical patients.

  

The few SF-36 responses collected revealed no differences between medical and surgical patients.

A Trial of Therapy

The present data are useful in describing the landscape of post-ICU survival to patients and their families. The data demonstrated a higher mortality trend that correlated with increases in age and increases in ICU duration and readmission. Within this continuum, there was no break point at which survivors and nonsurvivors clearly separated. The data therefore lack a boundary that can be used to define a trial of therapy. However, the added risks of age and recovery longer than 1 week are clear and should be included in care decisions. The generally better survival of surgical patients (nearly all of whom had elective surgery) in comparison with medical patients suggests these populations should be considered separately.

In the absence of a point distinguishing survivors from nonsurvivors, the authors performed a more detailed analysis of patients in the ICU for more than 14 days to provide some perspective on health care dependence in the subsequent year. That ICU survival does not necessarily equate to overall survival and independence long after ICU residence is an important matter for patients and families to consider when making decisions about critical care residence. The 14-day LOS data, though using a fairly arbitrary time point, suggest that patients who cannot recover from critical illness in less than 14 days should be advised of the range of short- and long-term mortality and the likelihood of high dependence on medical care within the subsequent year.

The concepts of hospital-dependent patient and persistent inflammation, immunosuppression, and catabolism syndrome have been introduced to describe the condition of progressive deterioration and inability to regain full independence after illness.^32,35 These illness patterns deserve attention in prognosis discussions. The present study focused not on ICU survival but on 1-year mortality and functional independence, and it is these longer term outcomes that critical care professionals should consider. Intensive care units are successful in improving short-term survival, but a long line of successful ICU discharges may lead an intensivist to think that longer term survival is important as well and convey this impression to patients and their families.

Study Strengths

This study is one of a few to investigate the short- and long-term survival of an unselected cohort of critically ill patients and is unique in its inclusion of both medical and surgical patients receiving care in the same environment. Medical and surgical patients have different survival profiles that may necessitate separate studies of these subpopulations. However, the finding of different survival profiles under the same care highlights the intrinsic differences between these groups. Use of a 1.5-year study period allowed the authors to capture ICU patients with long LOS and to include multiple episodes of care provided by more than 10 different attending physicians. Therefore, these data likely were not influenced by any rare events or idiosyncrasies in practice styles. Further, the same teams of physicians and nurses cared for all the medical and surgical patients, and all unit-based protocols and quality improvement activities were applied to all patients.

Study Limitations

The intensive care patients come from a large catchment area; however, conditions seen in tertiary referral centers, such as bone marrow transplants, cerebrovascular, transplant surgery, and ventricular-assist devices are not represented in this population.

In this study, bed days were used as a crude measure of care burden. From a nursing perspective, however, the workload may be higher with quick-turnover beds than with long-term residents. On the other hand, long-term ICU residents are visited by multiple consultants and receive a much larger set of interventions, including weeks of ventilation and hemodialysis, line changes, and family meetings. A comparison of the costs involved for different ICU subpopulations would add valuable information to this discussion.

The authors took a conservative approach in establishing the mortality and residence of patients 1 year after their ICU stays. At 6 months, 1-year patients without evidence of hospital or nursing facility residence were assumed to be home. In reality, nearly all these patients had multiple admissions or emergency department stays, or there was other evidence of intensive care. Some patients who were assumed to be home may have left the area and become untraceable. All estimates of care dependence and mortality should therefore be considered minimums. The authors cannot envision how any of their estimates could overstate the morbidity and mortality.

 

The concept of hospital dependence is applicable to the majority of the ICU survivors, though the authors did not attempt to create a quantitative measure of this status.³⁶ Another study limitation is that absence of hospitalization does not equal functional independence. A better definition of this status, and its application to a broad spectrum of LOS, would be a valuable adjunct to ICU decision making.

The convention by which the authors considered the first day of their study period a “fresh slate” did not adjust for the situation that some first admissions actually were readmissions. Assuming the validity of the finding that readmitted patients had a higher burden of morbidity and mortality, misclassification of admission status would tend to inflate the mortality of single-admission patients and minimize the magnitude of the differences found in this study. Similarly, an admission near the end of the study period may have been analyzed as a single admission, even if the patient was readmitted and died the next year. The latter situation also would tend to inflate the mortality of the single-admission category. None of these possible mathematical errors negates the fact that a second ICU admission should be regarded as a marker for poor recovery.

A more accurate estimate of short- and long-term prognosis likely can be obtained by examining laboratory studies and interventions such as vasopressors, dialysis, and ventilation at defined time points. Although the authors did not attempt it, development of such a model would be a valuable undertaking. They focused on describing the expected course of ICU patients and determining what patterns emerged from care duration. As this study found that the prognosis for long-term ICU residents remained guarded a long time after discharge, survival models of patients with 1- to 2-week ICU residences likely would be valuable in clinical decision making.

A quality-of-life survey was administered only to patients in the ICU longer than 2 weeks. This limited study was conducted to explore the feasibility of assessing outcomes other than survival and to determine the staffing requirements needed to research this further. A more meaningful analysis would come from a broader analysis of scores from 3 or 4 different ICU lengths of stay.

Clinician and family behavior can influence some of the outcomes measured in this study—particularly in cases in which an illness is poorly characterized and an evidence basis for decision making is lacking. In these situations, values and individual clinician judgment likely predominate, possibly introducing variability to care duration. Nevertheless, cumulative mortality 1 month or more after ICU residence would eliminate biased clinician behavior. The heterogeneity of care providers’ and families’ decision making, captured in this analysis, likely is a normal phenomenon that should help inform physicians’ understanding of prolonged ICU residence. 

Admission to an intensive care unit (ICU) is lifesaving for some patients, but for many, the admission carries high expectations and financial costs and fails to provide desirable outcomes. Patients who receive intensive care have a mortality rate of about 20%, and the costs of this care comprise about 4% of the U.S. health care budget.^1,2 In a study of Medicare recipients, treatment intensity and expenses increased between the mid-1980s and 1999 but without any increase in survivorship; per capita ICU expenses were higher for patients who did not survive the ICU.³ Use of the ICU in patients’ final stages of life has increased in proportion since then, and the demand for critical care is likely to continue as the relative proportion of elderly patients in the population rises.^2,4,5

Physicians and nurses who responded to a European survey on the inappropriateness of intensive care overwhelmingly endorsed the problems of “too much care” (89%) and “other patients would benefit more” (38%).⁶ Receiving terminal care in the ICU runs counter to the preferences of most patients.⁷ Therefore, the challenges are to define the true beneficiaries of critical care and to minimize the discomfort and unrealistic expectations of patients who will not benefit from intensive care.

For ICU patients, morbidity and mortality depend on the interaction of an acute insult (or a surgery), major comorbidities, and physiologic reserve. Aside from those with objective criteria of extreme illness, many patients have an indeterminate prognosis that is difficult to reliably predict.^8,9 Several prognostic scores, including the APACHE (Acute Physiologic Assessment and Chronic Health Evaluation) and SOFA (Sequential Organ Failure Assessment) scores, have proved useful in understanding the illness burden of a population when comparing outcomes in different ICUs. Yet their use in assessing the survival of individual patients has not been advocated.^10-15 The utility of such models is further challenged by the significant differences in survival between patients with similar illness scores; by the sometimes poor applicability of a model’s derivation cohort to other ICU populations (surgical in particular); by cases of huge disparities between actual and predicted mortality; and by the periodic need to recalibrate models according to advances in care.^16-20

 

Physician intuition regarding prognosis is highly variable. In a series of medical (floor and ICU) admissions, resident physician estimates of illness severity and postdischarge status were associated with stepwise differences in mortality and APACHE scores.^21,22 However, in a pure ICU population, in most cases seasoned providers could not accurately predict a patient’s chance of survival.²³ Physicians are likewise poor in predicting family preferences regarding aggressive care vs alternatives, and often, survival is couched in terms of ICU survival, which for family members may not be as meaningful as long-term survival or functional recovery. Further, quality of life and patient preferences are not discussed in most cases, even those associated with poor outcomes.²⁴ There also is a large amount of heterogeneity in the end of-life care of ICU patients. For example, cardiopulmonary resuscitation was attempted in up to 70% of dying patients in some ICUs and in as little as 4% in other ICUs.²⁵ Thus, the limitations of predictive models, combined with misperceptions of patient preference, poor communication, and local traditions, lead to aggressive care being given to patients who might not benefit from or desire such care.

It has been stated that the trajectory of most critical illness is unclear enough so that patients should be admitted to the ICU for a trial of therapy, and that in outcome predictions, the response to intensive treatment may be more useful than laboratory and other data comprising illness severity scores.^15,26 However, there is no consensus as to what constitutes a trial of intensive care therapy—vs a round of chemotherapy, a course of antibiotics, or a palliative ileostomy—yet this is the basis of many ICU admissions. Slight corrections in laboratory or physiologic findings often lead to continuation of aggressive care, often without any discussion of expected outcomes and the process of identifying and caring for patients who do not respond to therapy. Intensive care also may be prolonged because of several medical, personal, and social factors (Table 1).

At best, deciding how long to provide intensive care involves a synthesis of information about the trajectory, physiologic reserve, beliefs, values, and preferences of the patient. Any or all of these elements may not be known to the care decision-makers.

The authors conducted a study to determine whether a particular duration of care exists that represents a reasonable trial of therapy. As the VA Palo Alto Health Care System (VAPAHCS) ICU treats both medical and surgical patients, the authors were able to compare these subpopulations’ outcomes while providing the same standard of care. They analyzed the aggregate of patients as well as the medical and surgical subpopulations.

 

Methods

The VA Research and Development Committee and the Stanford Panel on Human Subjects approved the authors’ data collection and reporting.The study was conducted at the 15-bed mixed-medical/surgical VAPAHCS ICU. Analyzed data were drawn from all patients admitted during a 19-month period (July 14, 2008, to January 28, 2010). A serial log was used to prospectively capture basic data regarding each admission. Medical patients received care from the ICU service, and surgical patients were comanaged by the surgical and ICU teams.

A mortality database was constructed with data from the Decedent Affairs Office and from the national VistA database. The data included all deaths recorded either inside or outside the hospital or systemwide nursing facility. Mortality reported in the Computerized Patient Record System (CPRS) was queried further for patients with a length of stay (LOS) of more than 14 days.

Statistical Analysis

Calculations were based on denominators of individual patients or on number of admissions. All mortality calculations were based on a denominator of individual patients. For mortality analysis, only the last admission was included, unless a patient survived a full year between admissions. The Kruskal-Wallis test for nonnormally distributed data and the Dunn posttest for multiple comparisons were used for continuous variables (eg, age, LOS, risk scores); the Fisher exact test was used for categorical data; and the log-rank test was used to compare survival curves. For all analyses, P < .05 was considered statistically significant.

Mortality and Functional Status

Mortality risk scores on ICU admission were calculated with the Mortality Prediction Model–Admission III (MPM-III), using data from the CPRS. Specifics on this calculation are described in the eAppendix.

Current survival status of patients who were in the ICU more than 14 days was determined from the CPRS and telephone discussions with the patient or with relatives. Functional status was evaluated with the 36-Item Short Form Health Survey (SF-36), which has been used in comparable studies.^27,28 Disposition at 6 months and 1 year was established by inspecting the CPRS for dates corresponding to these exact periods. For example, a patient in the hospital about 1 year after ICU discharge would be considered to be at home if discharged 1 day before the 365-day anniversary. In a few cases, progress notes indicated that the patient was receiving around-the-clock nursing care at home; in the analysis, these cases were included with those of patients known to be in traditional nursing facilities. In cases in which the CPRS lacked mortality information, the patient was presumed to be alive even if there were no records of clinic visits or other medical attention. Serial admission data from a mixed-medical/surgical ICU were collected over a 19-month period (July 14, 2008, to January 28, 2010) and analyzed.

Results

The final data set consisted of 1,113 admissions and 976 patients (one-third medical, two-thirds surgical). In this cohort, 12% of all patients studied were readmitted to the ICU at least once, and 12% of all ICU admissions were repeat admissions. The medical/surgical proportion was similar for readmitted patients. Demographics and other data are available in eTable 1.

Length of Stay

The distribution of all patients by LOS in the study period is shown in eFigure 1A. Data are skewed rightward toward longer LOS. The median LOS of 3 days for the entire population differed according to specialty, with a median of 3 days for medical patients (interquartile range, 2-7 days) and a median of 2 days for surgical patients (interquartile range, 1-5 days; P < .01 for medical vs surgical patients).

The LOS differed between ICU patients admitted for the first time and those readmitted within the 19-month study period. For both admission categories, LOS was longer for medical patients than for surgical patients. However, there were no significant differences between percentages of medical and surgical patients who were readmitted (Table 2). Despite comprising about 12% of the population, patients with more than 1 admission accounted for 23% of admissions and 25% of all bed occupancies during the study period.

Figure e1B shows ICU bed occupancy for different LOS intervals (calculated as bed days) and indicates that despite accounting for a small percentage of admissions, patients with long LOS accounted for a significant portion of total occupancy (32% for more than 1 month, 45% for more than 14 days). The medical and surgical contributions of these long-LOS patients were about equal. The figures indicate that more than half of medical ICU patient occupancy involved LOS of more than 14 days, while surgical patients tended to have shorter LOS.

 

Mortality

Of all the patients in this study, 5.1% died in the ICU; the mortality rate was 11% for medical patients and 2.1% for surgical patients. Thirty days after discharge, overall mortality was 10.4%, or 23.5% for medical patients and 3.9% for surgical patients. Finally, 1 year after discharge, mortality rates were 21.5% (overall), 39.4% (medical patients), and 12.5% (surgical patients) (Table 3). Survival curves demonstrated the difference between medical and surgical patients at 30 days and 1 year (Figures 1A & 1B).

 

Impact of LOS on Mortality

One-year mortality was 17% for patients who were in the ICU less than 14 days and 40% for those in the ICU more than 14 days (relative risk [RR] = 2.35; P < .01) (Table 4).

In the under-14-days group, mortality was significantly higher (RR = 3.3; P < .0001) for medical patients (33%) than for surgical patients (10%). A significant association between LOS and mortality was found for admissions of 0 to 7 days (r² = 0.63; P < .05) and up to 6 weeks (r² = 0.88; P < .01) (Figures 2A & 2B). At each LOS, mortality was significantly higher for medical patients than for surgical patients.

Survival curves of both medical and surgical patients with LOS of 0 to 7 days, 8 to 14 days, and more than 14 days showed a similar significantly higher mortality rate associated with longer ICU duration (Figures 3A & 3B).

Mortality also was higher in patients with more than 1 ICU admission. For the aggregate of ICU patients, readmission status was significantly associated with a 10% increase in mortality. For both single- and multiple-admission status, the mortality rate was 2.5-fold higher for medical patients than for surgical patients. The increased mortality associated with readmission status was not significantly different for either medical or surgical patients analyzed as subgroups (eAppendix Table.)

Impact of Age on Mortality

Figures 4A and 4B shows 30-day and 1-year mortality associated with age; regression analysis indicated that age is an independent predictor of ICU mortality. For 30-day mortality, increased age was positively associated with mortality in medical patients but not in surgical patients (r² = 0.91; P < .0001). Age had a significant impact on 1-year mortality for both medical and surgical patients but less so in the latter (r² = 0.95 and 0.65, respectively; P < .001 for both). Although increased mortality was associated with both LOS and age, there was no clear association between the latter 2 variables.

Survival of Chronic Critical Illness

As eTable 2 shows, 21.5% of all patients died either in the ICU or within the first year after ICU discharge. To evaluate the survival of chronic ICU residence, the authors performed a detailed analysis of functional status and mortality of patients with LOS of more than 14 days. Seventy-one patients fit that profile (their mean LOS was 41 days; median, 28 days). Of these patients, 11 died in the ICU, and another 17 died within 6 months (including 2 in a stepdown unit and 7 in hospice). Overall, 28 (39%) of the 71 patients died either in the ICU or within 6 months (35% aggregate, 53% of medical patients, and 27% of surgical patients in ICU > 2 weeks). Another 8 patients (11%) died between 6 and 12 months after discharge. One-year mortality among patients in the ICU more than 14 days was 40% overall, 50% for medical patients, and 29% for surgical patients—or twice that predicted by the MPM-III model, which figured mortality rates of 25% and 12% for medical and surgical patients, respectively. In this cohort, the mean MPM-III score was 18.6% for 1-year survivors and 29.3% for nonsurvivors (P = .016, Mann-Whitney U test). Mortality was associated with a trend toward higher MPM-III scores in both medical and surgical patients but did not reach statistical significance.

Of the cohort patients who lived at least 6 months after ICU discharge, 45% were still in a hospital or were in a nursing facility at 6 months. Of the patients who lived at least 1 year, 33% were still in a hospital or were in a nursing facility (Figure 5). At 1 year, mean age was 63 years for survivors and 69 years for the deceased (P < .01 by Student t test).

There were no significant associations among age, LOS, and nursing facility residence at 1 year. Compared with surgical patients, a larger percentage of medical patients required skilled nursing at 1 year (RR = 1.95; P = .042 by Fisher exact test).

 

Quality-of-Life Survey

The authors successfully contacted 32 of the 39 patients who lived at least 1 year after discharge after an ICU stay of more than 14 days. The subgroups’ median SF-36 scores were similar: 57 for medical patients and 51 for surgical patients. These average scores over 8 domains are similar to those reported by Graf and colleagues for 9 months after ICU discharge (53.7) and are lower than the normative data reported by those authors for the German population (mean, 66.5).²⁹

Discussion

The goals of the present study were 2-fold—to gain a better understanding of the survival and functioning of patients after ICU residence and to define what may constitute a trial of therapy in ICU, or specifically to determine whether there is a particular ICU interval or point at which further care fails to improve survival. The study also compared medical and surgical subpopulations.

The main finding of this study was a 4-fold difference between ICU mortality and 1-year mortality. This mortality increase occurred in both medical and surgical patients, but there were large differences in magnitude between these groups. The survival rates generally were better than those of other general intensive care populations, though such a comparison should be made with caution, as survival differs by country, population, admitting practices, and a variety of other hospital characteristics.^30,31 Although some findings of the present study may relate to its largely male U.S. veteran population, the authors believe they have provided a data-collection-and-analysis model that can be used by any hospital trying to understand the course and outcome of its ICU patients and recognizing the value of this knowledge in discussions on goals of care.

Mortality and LOS

As each interval of ICU residence was associated with a stepwise increase in mortality, there was no clear cutoff for diminishing return. To create a reference point, the authors analyzed the data of patients who were in an ICU more than 14 days—thinking that this duration may represent an outer limit of a reasonable trial of therapy and a measure that probably distinguishes acute from chronic critical illness.³² Use of this interval represented a conservative approach, as only 6.5% of the patients in this cohort had a LOS of more than 14 days. This small percentage of patients accounted for 45% of total bed occupancy in this study and 54% of all medical bed occupancy. In the more-than-14-days group, mortality was 37.5% for surgical patients and 46.3% for medical patients. Thus, LOS may be a dynamic measure of physiologic reserve and disease severity—reflecting variables such as response to therapy, severity of comorbidities, resistance to new problems, and rebound from chronic stress, inflammation, and catabolism. This view is supported by the nearly 2-fold higher mortality in medical patients and nearly 3-fold higher mortality in surgical patients in comparison with MPM-III predictions.

Twelve percent of all patients were admitted to ICU multiple times, and these admissions accounted for 25% of all bed occupancies. Multiple admissions indicate a high disease burden or a low physiologic reserve that prevents full recovery from critical illness. As mortality was higher in patients with multiple admissions, ICU readmission should be regarded as a marker for poor overall recovery and should prompt consideration of both initial discharge criteria and trajectory as well as goals of care.

Medical vs Surgical Patients

In this cohort, medical and surgical patients were distinguished on several grounds. Despite the similar mean age of these subpopulations, medical patients had longer LOS and higher short- and long-term mortality. These findings are not surprising, as medical patients in the ICU have high rates of end-stage disease, malignancy, and high comorbidity burden and are often admitted to have potentially life-ending conditions stabilized. Surgical patients generally are selected on their ability to withstand major systemic perturbations—palliative and emergency operations excepted—and generally have medical conditions optimized before surgery. As the expectation of postoperative survival likely biases clinician behavior toward aggressive care, some short-term survival may reflect this behavior.

In contrast, such biased behavior is not an issue in 1-year survival, which instead accurately reflects underlying health. The different slopes of medical and surgical patients on age-vs-mortality in Figures 4A & 4B indicate the different physiologic makeups of these ICU patients. With short and long LOS compared, the difference between surgical and medical patients in the ICU is striking: Sixty-one percent of all surgical bed days vs 45% of all medical bed days are for LOS less than 14 days. Nevertheless, chronic critical illness has a significant impact on both medical and surgical patients and tends to equalize some of the survival differences between these groups. These populations had similar ICU readmission rates as well as similar higher mortality rates for LOS of more than 14 days and especially for LOS of more than 1 month. With longer LOS, the survival curve of surgical patients begins to resemble that of medical patients—suggesting that the phenotype of chronic critical illness becomes the dominant force influencing survival and function (Figures 3A & 3B). Indeed, for surgical patients, the highest mortality categories were ICU readmission and LOS of more than 30 days.

 

The mortality rate was significantly lower for surgical patients than it was for medical patients at all intervals studied, with the largest separation in the short-term categories of ICU and 30-day mortality. The post-ICU mortality rates for medical and surgical patients are similar to those reported in several other studies, including a study of veterans.^14-16,33,34 Among the present patients with LOS of more than 14 days, surviving surgical patients were significantly younger than nonsurviving surgical patients and both surviving and nonsurviving medical patients.

  

The few SF-36 responses collected revealed no differences between medical and surgical patients.

A Trial of Therapy

The present data are useful in describing the landscape of post-ICU survival to patients and their families. The data demonstrated a higher mortality trend that correlated with increases in age and increases in ICU duration and readmission. Within this continuum, there was no break point at which survivors and nonsurvivors clearly separated. The data therefore lack a boundary that can be used to define a trial of therapy. However, the added risks of age and recovery longer than 1 week are clear and should be included in care decisions. The generally better survival of surgical patients (nearly all of whom had elective surgery) in comparison with medical patients suggests these populations should be considered separately.

In the absence of a point distinguishing survivors from nonsurvivors, the authors performed a more detailed analysis of patients in the ICU for more than 14 days to provide some perspective on health care dependence in the subsequent year. That ICU survival does not necessarily equate to overall survival and independence long after ICU residence is an important matter for patients and families to consider when making decisions about critical care residence. The 14-day LOS data, though using a fairly arbitrary time point, suggest that patients who cannot recover from critical illness in less than 14 days should be advised of the range of short- and long-term mortality and the likelihood of high dependence on medical care within the subsequent year.

The concepts of hospital-dependent patient and persistent inflammation, immunosuppression, and catabolism syndrome have been introduced to describe the condition of progressive deterioration and inability to regain full independence after illness.^32,35 These illness patterns deserve attention in prognosis discussions. The present study focused not on ICU survival but on 1-year mortality and functional independence, and it is these longer term outcomes that critical care professionals should consider. Intensive care units are successful in improving short-term survival, but a long line of successful ICU discharges may lead an intensivist to think that longer term survival is important as well and convey this impression to patients and their families.

Study Strengths

This study is one of a few to investigate the short- and long-term survival of an unselected cohort of critically ill patients and is unique in its inclusion of both medical and surgical patients receiving care in the same environment. Medical and surgical patients have different survival profiles that may necessitate separate studies of these subpopulations. However, the finding of different survival profiles under the same care highlights the intrinsic differences between these groups. Use of a 1.5-year study period allowed the authors to capture ICU patients with long LOS and to include multiple episodes of care provided by more than 10 different attending physicians. Therefore, these data likely were not influenced by any rare events or idiosyncrasies in practice styles. Further, the same teams of physicians and nurses cared for all the medical and surgical patients, and all unit-based protocols and quality improvement activities were applied to all patients.

Study Limitations

The intensive care patients come from a large catchment area; however, conditions seen in tertiary referral centers, such as bone marrow transplants, cerebrovascular, transplant surgery, and ventricular-assist devices are not represented in this population.

In this study, bed days were used as a crude measure of care burden. From a nursing perspective, however, the workload may be higher with quick-turnover beds than with long-term residents. On the other hand, long-term ICU residents are visited by multiple consultants and receive a much larger set of interventions, including weeks of ventilation and hemodialysis, line changes, and family meetings. A comparison of the costs involved for different ICU subpopulations would add valuable information to this discussion.

The authors took a conservative approach in establishing the mortality and residence of patients 1 year after their ICU stays. At 6 months, 1-year patients without evidence of hospital or nursing facility residence were assumed to be home. In reality, nearly all these patients had multiple admissions or emergency department stays, or there was other evidence of intensive care. Some patients who were assumed to be home may have left the area and become untraceable. All estimates of care dependence and mortality should therefore be considered minimums. The authors cannot envision how any of their estimates could overstate the morbidity and mortality.

 

The concept of hospital dependence is applicable to the majority of the ICU survivors, though the authors did not attempt to create a quantitative measure of this status.³⁶ Another study limitation is that absence of hospitalization does not equal functional independence. A better definition of this status, and its application to a broad spectrum of LOS, would be a valuable adjunct to ICU decision making.

The convention by which the authors considered the first day of their study period a “fresh slate” did not adjust for the situation that some first admissions actually were readmissions. Assuming the validity of the finding that readmitted patients had a higher burden of morbidity and mortality, misclassification of admission status would tend to inflate the mortality of single-admission patients and minimize the magnitude of the differences found in this study. Similarly, an admission near the end of the study period may have been analyzed as a single admission, even if the patient was readmitted and died the next year. The latter situation also would tend to inflate the mortality of the single-admission category. None of these possible mathematical errors negates the fact that a second ICU admission should be regarded as a marker for poor recovery.

A more accurate estimate of short- and long-term prognosis likely can be obtained by examining laboratory studies and interventions such as vasopressors, dialysis, and ventilation at defined time points. Although the authors did not attempt it, development of such a model would be a valuable undertaking. They focused on describing the expected course of ICU patients and determining what patterns emerged from care duration. As this study found that the prognosis for long-term ICU residents remained guarded a long time after discharge, survival models of patients with 1- to 2-week ICU residences likely would be valuable in clinical decision making.

A quality-of-life survey was administered only to patients in the ICU longer than 2 weeks. This limited study was conducted to explore the feasibility of assessing outcomes other than survival and to determine the staffing requirements needed to research this further. A more meaningful analysis would come from a broader analysis of scores from 3 or 4 different ICU lengths of stay.

Clinician and family behavior can influence some of the outcomes measured in this study—particularly in cases in which an illness is poorly characterized and an evidence basis for decision making is lacking. In these situations, values and individual clinician judgment likely predominate, possibly introducing variability to care duration. Nevertheless, cumulative mortality 1 month or more after ICU residence would eliminate biased clinician behavior. The heterogeneity of care providers’ and families’ decision making, captured in this analysis, likely is a normal phenomenon that should help inform physicians’ understanding of prolonged ICU residence. 

Multiple sclerosis (MS) is a disorder characterized by inflammation, demyelination, and degeneration of the central nervous system (CNS). The hallmark of the disorder is relapses and remissions of neurologic symptoms occurring early in the disease course, which are often associated with areas of CNS inflammation and myelin loss.^1-3 The inciting cause for this inflammation is unknown but is believed to be multifactorial, with environmental and genetic influences creating an adaptive, T cell-mediated autoimmune response against the CNS.⁴ Separate from the acute attacks, progressive neurodegeneration can occur more chronically and is characterized by axonal loss and grey matter atrophy thought to be due to direct cytotoxic activity of the innate immune system as well as toxic intermediates, such as nitric oxide.^4,5 Despite the multiple insults early on, neurologic disability typically becomes more apparent over time.⁶ The disability threshold theory argues that neurologic function compensates for brain tissue loss until a threshold of accumulated damage is exceeded.⁷

Background

The incidence of MS follows a geographic gradient; rates rise as the distance from the equator increases.^8,9 This is thought to be due to the gradient of relative sun exposure and its role in the production of vitamin D, which plays an important role in immune regulation when converted to its active hormonal form. Multiple sclerosis is more prevalent in non-Hispanic white patients than it is in other racial groups, and women are affected nearly 2 to 3 times more often than are men.¹⁰ About 450,000 individuals in the U.S. and more than 2 million worldwide have MS.^11-14

Multiple sclerosis is the most common cause of nontraumatic neurologic disability in young adults. It is typically diagnosed in the third and fourth decades of life, and those who are diagnosed after age 50 years often can recount neurologic symptoms that began years before. However, pediatric-onset and new-onset cases in the elderly have been reported. It has been estimated that up to 10% of patients with MS have onset before 18 years of age.^15-17 Compared with adult-onset MS, pediatric-onset is associated with a longer period between initial attack and physical disability, although the average age of disability onset is about 10 years younger.^17,18

Disease Courses

Relapsing-remitting MS (RRMS) is the most common disease course overall, and this pattern affects 97% of individuals with disease onset before age 18 years.^15-17 The clinically isolated syndrome disease course leads to clinically definite MS in one-third of patients within 1 year and in one-half of patients within 2 years.¹⁹ In the majority of cases, the RRMS course transitions over time to secondary-progressive MS (SPMS), which is a disease pattern of progressively worsening disability with few neurologic relapses. Although inflammation is present at all stages, the difference is in the predominance of cell types involved.⁵ Why the shift from active to chronic inflammation occurs and how to prevent it remain central questions in MS research.⁴ Regardless, tentative evidence suggests that prevention of relapses may reduce disability accumulation and risk of conversion to progressive MS.²⁰

A minority of patients with MS are diagnosed with primary-progressive MS (PPMS) at onset, which is characterized by a disease pattern that follows a relatively steady progression of neurologic symptoms over time, without clear relapses or remissions of these symptoms, though phases of stability or fluctuations in disability may still occur.²¹ It is typically diagnosed at an older age than is RRMS, and it is rare in children; suspicion of PPMS in this age group should prompt detailed assessment of alternative diagnoses.^17,22 Primary-progressive MS is more equally distributed in men and women than is RRMS.

Regardless of onset type, disability progression seems to occur at the same rate among all patients with MS after a certain threshold is reached. The established assessment scale for disability progression in MS is the Kurtzke Expanded Disability Status scale (EDSS), which has a scoring range from 0 to 10. Data from several patient registries have shown that once EDSS step 4 is reached, progression thereafter occurs at a predictable rate that is similar across MS phenotypes.²³ The time it takes patients to subsequently reach higher EDSS steps may be independent of preceding factors.²³

MS Symptom Burden

The neurologic symptoms that patients experience are fluctuating and disabling throughout the disease course, irrespective of onset type. Typical MS symptoms include mobility impairment, changes in cognition and mood, pain and other sensation disturbances, bowel and bladder dysfunction, fatigue, and visual disturbances. The burden of these symptoms can significantly impact quality of life (QOL) for patients and their families. The symptom burden can pose a direct threat to a patient’s autonomy, necessitating adaptation to an unpredictable disease that requires frequent health care visits to many different health care providers (eg, neurologists; primary care providers; physiatrists; urologists; ophthalmologists; and speech, physical, and occupational therapists), periodic testing, and often costly medications.²⁴

 

Compared with patients who have other chronic conditions, patients with MS experience diminished societal roles, along with decreased assessments in health, energy, and physical functions.²⁵ These often lead to early exit from the workforce and limitations in household responsibilities, which further impact QOL.²⁶ Including both direct and indirect costs of the disease, a patient with MS can expect a lifetime financial burden of nearly $1.2 million.²⁷

Large population cohort studies in MS, along with MS registry studies of patients untreated with disease-modifying therapies, have shown reduced survival rates by an average of 7 to 14 years.^23,28 Multiple sclerosis is the main cause of death in about 50% of cases (EDSS step 10), which is defined as “acute death due to brain stem involvement or to respiratory failure, or death consequent to the chronic bedridden state with terminal pneumonia, sepsis, uremia, or cardiorespiratory failure [and excluding] intercurrent causes of death.”²³ For the remaining patients with MS, cause of death is similar to those of the general population, such as cardiovascular disease and cancer.²³ However, the incidence of suicide is higher among patients with MS.²³

All these factors underscore the importance of early diagnosis as well as early initiation of effective disease-modifying therapy.

The diagnosis of MS is difficult largely due to the lack of definitive diagnostic testing and specific biomarkers for disease activity and because of the wide range of differential diagnoses that can mimic MS.^19,21,29 Diagnosis of MS requires that more likely diagnoses have been excluded as well as that lesions (scleroses) are disseminated in space within the CNS and disseminated in time. The 2010 Revised McDonald Diagnostic Criteria for MS are outlined in Table 1.

Disease-Modifying Therapies

The goal of MS disease-modifying therapy is to reduce the early clinical and subclinical disease activity that eventually contributes to long-term disability.^31,32 There are currently 13 FDA-approved disease-modifying therapies for MS. These include 7 self-injecting therapies, 3 oral therapies, and 3 infusion therapies. These 13 medications have 8 different mechanisms of action (MOA) that target distinct areas of the immune-mediated disease process. They also differ in their frequencies and routes of administration in addition to their adverse effect (AE) profiles (Tables 2, 3, and 4).

 

 

Treatment Considerations

In 1993, interferon beta-1b became the first FDA-approved MS medication. In the following 2 decades, there became 12 additional FDA-approved medications for MS, beginning with other injectables. The first infusion therapy was introduced in 2004, followed by various oral medications. The treatment landscape continues to change rapidly. This therapeutic revolution has occurred largely due to the improved understanding of the pathophysiology of MS and unquestionably has improved the prognosis and overall QOL for patients. The question is no longer how to treat MS but rather how to personalize and optimize treatment for each patient.²⁰

Despite all available treatment options, none are curative, and none have been proven to offer neuroprotection or contribute to neural repair. To date, no studies have led to FDA-approved therapies for PPMS. Further, the efficacy of any of these medications varies from patient to patient. Due largely to the lack of biomarkers for disease activity and treatment response, drug efficacy continues to be measured according to the current gold standard, which is identification of gadolinium-enhancing lesions in the white matter on magnetic resonance imaging (MRI), combined with other markers of disease, including clinical relapse rate and confirmed disability progression.¹⁹ In general, the injectable therapies are expected to protect against about 20% to 35% of relapses; the oral agents, 50% to 55%; and the infusion therapies, > 60%.²

In conjunction with a medication’s efficacy rate and safety profile, the frequency and route of administration also must be considered. In general, MS medications are exceedingly expensive, some costing up to tens-of-thousands of dollars per year.⁴⁸ All these factors have the real potential to negatively impact patient adherence. Nonadherence and gaps in treatment have been correlated with increased rates of relapses and progression of disability as well as negative MRI outcomes.^49-53

When to Initiate Treatment

Once a patient is diagnosed, a common question is, when is the right time to initiate treatment? The primary target of the current MS medications is to decrease CNS inflammation (relapses). The ideal time to start treatment is as promptly as possible after confirmation of the diagnosis to combat the early inflammatory relapsing phase of the disease. There seems to be an early window in the disease course when achieving disease control can have a profound impact on long-term disability. Disease control is typically defined as decreasing relapses, slowing the accumulation of lesions visualized on MRI, and preventing the disability that occurs from both incomplete recovery after relapses and overall disease progression.^54,55

 

Certain clinical indicators, such as higher relapse rates early in the disease course and MRI characteristics, including total lesion burden and the location of lesions within the CNS, seem to be associated with a higher risk of disease progression.⁵⁶ These are potential prognostic indicators that can help tailor the choice of disease-modifying therapy for patients.⁵⁷ Those with highly inflammatory and potentially aggressive disease at onset, for example, may benefit from early initiation of higher efficacy therapies, whereas those with more benign forms of MS at onset may fare well on lower efficacy therapies. In general, when it comes to currently available MS treatments, higher efficacy is often tied to riskier AE profiles, so the best medication may be the “least efficacious” one that can still control the disease.²⁰

Hauser and colleagues suggested a treatment decision-making model that identifies the interferons, glatiramer acetate, dimethyl fumarate, and teriflunomide as acceptable first-line therapies; fingolimod and natalizumab as acceptable second-line options; and mitoxantrone and alemtuzumab as acceptable third-line therapeutic options.²⁰ The authors generally agree with Hauser and colleagues’ model, and it is important to consider individual patient factors (eg, comorbidities, concurrent medications, life circumstances) and disease severity when deciding on a treatment plan.

Perhaps an even more difficult question is, when is the right time to switch therapies? There remains a dearth of either guidelines or comparative studies for treatment management decisions. Further, without reliable biomarkers, the clinical and pathologic heterogeneity of MS makes treatment difficult.^4,19 In practice, there is general consensus that 1 year of treatment monitoring for effects on clinical and radiologic outcomes is an acceptable time frame to evaluate effectiveness of a disease-modifying treatment. If adherence is maintained and there is still evidence of clinical or MRI activity (suggesting a suboptimal response), an alternative therapy, particularly one with a different MOA, should be strongly considered. This highlights the importance of broad access to all available MS therapies to allow for early selection of a correct therapy that patients will remain adherent to and that controls their disease.

Conclusion

Multiple sclerosis remains a highly unpredictable disease, and relapses have the ability to produce a measurable and sustained impact on the level of disability.⁵⁸ Still, the influence of reduced relapses on preventing disability in an individual patient remains unclear. Large, long-term, prospective cohort studies may clarify whether early treatment affects disease progression and disability.²⁰ However, it is quite evident that effective relapse reduction decreases discomfort, reduces days lost from work and other important activities of daily life, and improves QOL.^58,59

There is still much to learn about this unique disease, but emerging evidence in the medical literature highlights the importance of setting treatment goals that include targeting disease activity to achieve early and effective control. Attaining control with a MS medication seems to be a key component of slowing the physical and emotional disability that can accumulate, helping patients remain active and maintain the highest QOL possible for as long as possible.

Multiple sclerosis (MS) is a disorder characterized by inflammation, demyelination, and degeneration of the central nervous system (CNS). The hallmark of the disorder is relapses and remissions of neurologic symptoms occurring early in the disease course, which are often associated with areas of CNS inflammation and myelin loss.^1-3 The inciting cause for this inflammation is unknown but is believed to be multifactorial, with environmental and genetic influences creating an adaptive, T cell-mediated autoimmune response against the CNS.⁴ Separate from the acute attacks, progressive neurodegeneration can occur more chronically and is characterized by axonal loss and grey matter atrophy thought to be due to direct cytotoxic activity of the innate immune system as well as toxic intermediates, such as nitric oxide.^4,5 Despite the multiple insults early on, neurologic disability typically becomes more apparent over time.⁶ The disability threshold theory argues that neurologic function compensates for brain tissue loss until a threshold of accumulated damage is exceeded.⁷

Background

The incidence of MS follows a geographic gradient; rates rise as the distance from the equator increases.^8,9 This is thought to be due to the gradient of relative sun exposure and its role in the production of vitamin D, which plays an important role in immune regulation when converted to its active hormonal form. Multiple sclerosis is more prevalent in non-Hispanic white patients than it is in other racial groups, and women are affected nearly 2 to 3 times more often than are men.¹⁰ About 450,000 individuals in the U.S. and more than 2 million worldwide have MS.^11-14

Multiple sclerosis is the most common cause of nontraumatic neurologic disability in young adults. It is typically diagnosed in the third and fourth decades of life, and those who are diagnosed after age 50 years often can recount neurologic symptoms that began years before. However, pediatric-onset and new-onset cases in the elderly have been reported. It has been estimated that up to 10% of patients with MS have onset before 18 years of age.^15-17 Compared with adult-onset MS, pediatric-onset is associated with a longer period between initial attack and physical disability, although the average age of disability onset is about 10 years younger.^17,18

Disease Courses

Relapsing-remitting MS (RRMS) is the most common disease course overall, and this pattern affects 97% of individuals with disease onset before age 18 years.^15-17 The clinically isolated syndrome disease course leads to clinically definite MS in one-third of patients within 1 year and in one-half of patients within 2 years.¹⁹ In the majority of cases, the RRMS course transitions over time to secondary-progressive MS (SPMS), which is a disease pattern of progressively worsening disability with few neurologic relapses. Although inflammation is present at all stages, the difference is in the predominance of cell types involved.⁵ Why the shift from active to chronic inflammation occurs and how to prevent it remain central questions in MS research.⁴ Regardless, tentative evidence suggests that prevention of relapses may reduce disability accumulation and risk of conversion to progressive MS.²⁰

A minority of patients with MS are diagnosed with primary-progressive MS (PPMS) at onset, which is characterized by a disease pattern that follows a relatively steady progression of neurologic symptoms over time, without clear relapses or remissions of these symptoms, though phases of stability or fluctuations in disability may still occur.²¹ It is typically diagnosed at an older age than is RRMS, and it is rare in children; suspicion of PPMS in this age group should prompt detailed assessment of alternative diagnoses.^17,22 Primary-progressive MS is more equally distributed in men and women than is RRMS.

Regardless of onset type, disability progression seems to occur at the same rate among all patients with MS after a certain threshold is reached. The established assessment scale for disability progression in MS is the Kurtzke Expanded Disability Status scale (EDSS), which has a scoring range from 0 to 10. Data from several patient registries have shown that once EDSS step 4 is reached, progression thereafter occurs at a predictable rate that is similar across MS phenotypes.²³ The time it takes patients to subsequently reach higher EDSS steps may be independent of preceding factors.²³

MS Symptom Burden

The neurologic symptoms that patients experience are fluctuating and disabling throughout the disease course, irrespective of onset type. Typical MS symptoms include mobility impairment, changes in cognition and mood, pain and other sensation disturbances, bowel and bladder dysfunction, fatigue, and visual disturbances. The burden of these symptoms can significantly impact quality of life (QOL) for patients and their families. The symptom burden can pose a direct threat to a patient’s autonomy, necessitating adaptation to an unpredictable disease that requires frequent health care visits to many different health care providers (eg, neurologists; primary care providers; physiatrists; urologists; ophthalmologists; and speech, physical, and occupational therapists), periodic testing, and often costly medications.²⁴

 

Compared with patients who have other chronic conditions, patients with MS experience diminished societal roles, along with decreased assessments in health, energy, and physical functions.²⁵ These often lead to early exit from the workforce and limitations in household responsibilities, which further impact QOL.²⁶ Including both direct and indirect costs of the disease, a patient with MS can expect a lifetime financial burden of nearly $1.2 million.²⁷

Large population cohort studies in MS, along with MS registry studies of patients untreated with disease-modifying therapies, have shown reduced survival rates by an average of 7 to 14 years.^23,28 Multiple sclerosis is the main cause of death in about 50% of cases (EDSS step 10), which is defined as “acute death due to brain stem involvement or to respiratory failure, or death consequent to the chronic bedridden state with terminal pneumonia, sepsis, uremia, or cardiorespiratory failure [and excluding] intercurrent causes of death.”²³ For the remaining patients with MS, cause of death is similar to those of the general population, such as cardiovascular disease and cancer.²³ However, the incidence of suicide is higher among patients with MS.²³

All these factors underscore the importance of early diagnosis as well as early initiation of effective disease-modifying therapy.

The diagnosis of MS is difficult largely due to the lack of definitive diagnostic testing and specific biomarkers for disease activity and because of the wide range of differential diagnoses that can mimic MS.^19,21,29 Diagnosis of MS requires that more likely diagnoses have been excluded as well as that lesions (scleroses) are disseminated in space within the CNS and disseminated in time. The 2010 Revised McDonald Diagnostic Criteria for MS are outlined in Table 1.

Disease-Modifying Therapies

The goal of MS disease-modifying therapy is to reduce the early clinical and subclinical disease activity that eventually contributes to long-term disability.^31,32 There are currently 13 FDA-approved disease-modifying therapies for MS. These include 7 self-injecting therapies, 3 oral therapies, and 3 infusion therapies. These 13 medications have 8 different mechanisms of action (MOA) that target distinct areas of the immune-mediated disease process. They also differ in their frequencies and routes of administration in addition to their adverse effect (AE) profiles (Tables 2, 3, and 4).

 

 

Treatment Considerations

In 1993, interferon beta-1b became the first FDA-approved MS medication. In the following 2 decades, there became 12 additional FDA-approved medications for MS, beginning with other injectables. The first infusion therapy was introduced in 2004, followed by various oral medications. The treatment landscape continues to change rapidly. This therapeutic revolution has occurred largely due to the improved understanding of the pathophysiology of MS and unquestionably has improved the prognosis and overall QOL for patients. The question is no longer how to treat MS but rather how to personalize and optimize treatment for each patient.²⁰

Despite all available treatment options, none are curative, and none have been proven to offer neuroprotection or contribute to neural repair. To date, no studies have led to FDA-approved therapies for PPMS. Further, the efficacy of any of these medications varies from patient to patient. Due largely to the lack of biomarkers for disease activity and treatment response, drug efficacy continues to be measured according to the current gold standard, which is identification of gadolinium-enhancing lesions in the white matter on magnetic resonance imaging (MRI), combined with other markers of disease, including clinical relapse rate and confirmed disability progression.¹⁹ In general, the injectable therapies are expected to protect against about 20% to 35% of relapses; the oral agents, 50% to 55%; and the infusion therapies, > 60%.²

In conjunction with a medication’s efficacy rate and safety profile, the frequency and route of administration also must be considered. In general, MS medications are exceedingly expensive, some costing up to tens-of-thousands of dollars per year.⁴⁸ All these factors have the real potential to negatively impact patient adherence. Nonadherence and gaps in treatment have been correlated with increased rates of relapses and progression of disability as well as negative MRI outcomes.^49-53

When to Initiate Treatment

Once a patient is diagnosed, a common question is, when is the right time to initiate treatment? The primary target of the current MS medications is to decrease CNS inflammation (relapses). The ideal time to start treatment is as promptly as possible after confirmation of the diagnosis to combat the early inflammatory relapsing phase of the disease. There seems to be an early window in the disease course when achieving disease control can have a profound impact on long-term disability. Disease control is typically defined as decreasing relapses, slowing the accumulation of lesions visualized on MRI, and preventing the disability that occurs from both incomplete recovery after relapses and overall disease progression.^54,55

 

Certain clinical indicators, such as higher relapse rates early in the disease course and MRI characteristics, including total lesion burden and the location of lesions within the CNS, seem to be associated with a higher risk of disease progression.⁵⁶ These are potential prognostic indicators that can help tailor the choice of disease-modifying therapy for patients.⁵⁷ Those with highly inflammatory and potentially aggressive disease at onset, for example, may benefit from early initiation of higher efficacy therapies, whereas those with more benign forms of MS at onset may fare well on lower efficacy therapies. In general, when it comes to currently available MS treatments, higher efficacy is often tied to riskier AE profiles, so the best medication may be the “least efficacious” one that can still control the disease.²⁰

Hauser and colleagues suggested a treatment decision-making model that identifies the interferons, glatiramer acetate, dimethyl fumarate, and teriflunomide as acceptable first-line therapies; fingolimod and natalizumab as acceptable second-line options; and mitoxantrone and alemtuzumab as acceptable third-line therapeutic options.²⁰ The authors generally agree with Hauser and colleagues’ model, and it is important to consider individual patient factors (eg, comorbidities, concurrent medications, life circumstances) and disease severity when deciding on a treatment plan.

Perhaps an even more difficult question is, when is the right time to switch therapies? There remains a dearth of either guidelines or comparative studies for treatment management decisions. Further, without reliable biomarkers, the clinical and pathologic heterogeneity of MS makes treatment difficult.^4,19 In practice, there is general consensus that 1 year of treatment monitoring for effects on clinical and radiologic outcomes is an acceptable time frame to evaluate effectiveness of a disease-modifying treatment. If adherence is maintained and there is still evidence of clinical or MRI activity (suggesting a suboptimal response), an alternative therapy, particularly one with a different MOA, should be strongly considered. This highlights the importance of broad access to all available MS therapies to allow for early selection of a correct therapy that patients will remain adherent to and that controls their disease.

Conclusion

Multiple sclerosis remains a highly unpredictable disease, and relapses have the ability to produce a measurable and sustained impact on the level of disability.⁵⁸ Still, the influence of reduced relapses on preventing disability in an individual patient remains unclear. Large, long-term, prospective cohort studies may clarify whether early treatment affects disease progression and disability.²⁰ However, it is quite evident that effective relapse reduction decreases discomfort, reduces days lost from work and other important activities of daily life, and improves QOL.^58,59

There is still much to learn about this unique disease, but emerging evidence in the medical literature highlights the importance of setting treatment goals that include targeting disease activity to achieve early and effective control. Attaining control with a MS medication seems to be a key component of slowing the physical and emotional disability that can accumulate, helping patients remain active and maintain the highest QOL possible for as long as possible.

Multiple sclerosis (MS) is a disorder characterized by inflammation, demyelination, and degeneration of the central nervous system (CNS). The hallmark of the disorder is relapses and remissions of neurologic symptoms occurring early in the disease course, which are often associated with areas of CNS inflammation and myelin loss.^1-3 The inciting cause for this inflammation is unknown but is believed to be multifactorial, with environmental and genetic influences creating an adaptive, T cell-mediated autoimmune response against the CNS.⁴ Separate from the acute attacks, progressive neurodegeneration can occur more chronically and is characterized by axonal loss and grey matter atrophy thought to be due to direct cytotoxic activity of the innate immune system as well as toxic intermediates, such as nitric oxide.^4,5 Despite the multiple insults early on, neurologic disability typically becomes more apparent over time.⁶ The disability threshold theory argues that neurologic function compensates for brain tissue loss until a threshold of accumulated damage is exceeded.⁷

Background

The incidence of MS follows a geographic gradient; rates rise as the distance from the equator increases.^8,9 This is thought to be due to the gradient of relative sun exposure and its role in the production of vitamin D, which plays an important role in immune regulation when converted to its active hormonal form. Multiple sclerosis is more prevalent in non-Hispanic white patients than it is in other racial groups, and women are affected nearly 2 to 3 times more often than are men.¹⁰ About 450,000 individuals in the U.S. and more than 2 million worldwide have MS.^11-14

Multiple sclerosis is the most common cause of nontraumatic neurologic disability in young adults. It is typically diagnosed in the third and fourth decades of life, and those who are diagnosed after age 50 years often can recount neurologic symptoms that began years before. However, pediatric-onset and new-onset cases in the elderly have been reported. It has been estimated that up to 10% of patients with MS have onset before 18 years of age.^15-17 Compared with adult-onset MS, pediatric-onset is associated with a longer period between initial attack and physical disability, although the average age of disability onset is about 10 years younger.^17,18

Disease Courses

Relapsing-remitting MS (RRMS) is the most common disease course overall, and this pattern affects 97% of individuals with disease onset before age 18 years.^15-17 The clinically isolated syndrome disease course leads to clinically definite MS in one-third of patients within 1 year and in one-half of patients within 2 years.¹⁹ In the majority of cases, the RRMS course transitions over time to secondary-progressive MS (SPMS), which is a disease pattern of progressively worsening disability with few neurologic relapses. Although inflammation is present at all stages, the difference is in the predominance of cell types involved.⁵ Why the shift from active to chronic inflammation occurs and how to prevent it remain central questions in MS research.⁴ Regardless, tentative evidence suggests that prevention of relapses may reduce disability accumulation and risk of conversion to progressive MS.²⁰

A minority of patients with MS are diagnosed with primary-progressive MS (PPMS) at onset, which is characterized by a disease pattern that follows a relatively steady progression of neurologic symptoms over time, without clear relapses or remissions of these symptoms, though phases of stability or fluctuations in disability may still occur.²¹ It is typically diagnosed at an older age than is RRMS, and it is rare in children; suspicion of PPMS in this age group should prompt detailed assessment of alternative diagnoses.^17,22 Primary-progressive MS is more equally distributed in men and women than is RRMS.

Regardless of onset type, disability progression seems to occur at the same rate among all patients with MS after a certain threshold is reached. The established assessment scale for disability progression in MS is the Kurtzke Expanded Disability Status scale (EDSS), which has a scoring range from 0 to 10. Data from several patient registries have shown that once EDSS step 4 is reached, progression thereafter occurs at a predictable rate that is similar across MS phenotypes.²³ The time it takes patients to subsequently reach higher EDSS steps may be independent of preceding factors.²³

MS Symptom Burden

The neurologic symptoms that patients experience are fluctuating and disabling throughout the disease course, irrespective of onset type. Typical MS symptoms include mobility impairment, changes in cognition and mood, pain and other sensation disturbances, bowel and bladder dysfunction, fatigue, and visual disturbances. The burden of these symptoms can significantly impact quality of life (QOL) for patients and their families. The symptom burden can pose a direct threat to a patient’s autonomy, necessitating adaptation to an unpredictable disease that requires frequent health care visits to many different health care providers (eg, neurologists; primary care providers; physiatrists; urologists; ophthalmologists; and speech, physical, and occupational therapists), periodic testing, and often costly medications.²⁴

 

Compared with patients who have other chronic conditions, patients with MS experience diminished societal roles, along with decreased assessments in health, energy, and physical functions.²⁵ These often lead to early exit from the workforce and limitations in household responsibilities, which further impact QOL.²⁶ Including both direct and indirect costs of the disease, a patient with MS can expect a lifetime financial burden of nearly $1.2 million.²⁷

Large population cohort studies in MS, along with MS registry studies of patients untreated with disease-modifying therapies, have shown reduced survival rates by an average of 7 to 14 years.^23,28 Multiple sclerosis is the main cause of death in about 50% of cases (EDSS step 10), which is defined as “acute death due to brain stem involvement or to respiratory failure, or death consequent to the chronic bedridden state with terminal pneumonia, sepsis, uremia, or cardiorespiratory failure [and excluding] intercurrent causes of death.”²³ For the remaining patients with MS, cause of death is similar to those of the general population, such as cardiovascular disease and cancer.²³ However, the incidence of suicide is higher among patients with MS.²³

All these factors underscore the importance of early diagnosis as well as early initiation of effective disease-modifying therapy.

The diagnosis of MS is difficult largely due to the lack of definitive diagnostic testing and specific biomarkers for disease activity and because of the wide range of differential diagnoses that can mimic MS.^19,21,29 Diagnosis of MS requires that more likely diagnoses have been excluded as well as that lesions (scleroses) are disseminated in space within the CNS and disseminated in time. The 2010 Revised McDonald Diagnostic Criteria for MS are outlined in Table 1.

Disease-Modifying Therapies

The goal of MS disease-modifying therapy is to reduce the early clinical and subclinical disease activity that eventually contributes to long-term disability.^31,32 There are currently 13 FDA-approved disease-modifying therapies for MS. These include 7 self-injecting therapies, 3 oral therapies, and 3 infusion therapies. These 13 medications have 8 different mechanisms of action (MOA) that target distinct areas of the immune-mediated disease process. They also differ in their frequencies and routes of administration in addition to their adverse effect (AE) profiles (Tables 2, 3, and 4).

 

 

Treatment Considerations

In 1993, interferon beta-1b became the first FDA-approved MS medication. In the following 2 decades, there became 12 additional FDA-approved medications for MS, beginning with other injectables. The first infusion therapy was introduced in 2004, followed by various oral medications. The treatment landscape continues to change rapidly. This therapeutic revolution has occurred largely due to the improved understanding of the pathophysiology of MS and unquestionably has improved the prognosis and overall QOL for patients. The question is no longer how to treat MS but rather how to personalize and optimize treatment for each patient.²⁰

Despite all available treatment options, none are curative, and none have been proven to offer neuroprotection or contribute to neural repair. To date, no studies have led to FDA-approved therapies for PPMS. Further, the efficacy of any of these medications varies from patient to patient. Due largely to the lack of biomarkers for disease activity and treatment response, drug efficacy continues to be measured according to the current gold standard, which is identification of gadolinium-enhancing lesions in the white matter on magnetic resonance imaging (MRI), combined with other markers of disease, including clinical relapse rate and confirmed disability progression.¹⁹ In general, the injectable therapies are expected to protect against about 20% to 35% of relapses; the oral agents, 50% to 55%; and the infusion therapies, > 60%.²

In conjunction with a medication’s efficacy rate and safety profile, the frequency and route of administration also must be considered. In general, MS medications are exceedingly expensive, some costing up to tens-of-thousands of dollars per year.⁴⁸ All these factors have the real potential to negatively impact patient adherence. Nonadherence and gaps in treatment have been correlated with increased rates of relapses and progression of disability as well as negative MRI outcomes.^49-53

When to Initiate Treatment

Once a patient is diagnosed, a common question is, when is the right time to initiate treatment? The primary target of the current MS medications is to decrease CNS inflammation (relapses). The ideal time to start treatment is as promptly as possible after confirmation of the diagnosis to combat the early inflammatory relapsing phase of the disease. There seems to be an early window in the disease course when achieving disease control can have a profound impact on long-term disability. Disease control is typically defined as decreasing relapses, slowing the accumulation of lesions visualized on MRI, and preventing the disability that occurs from both incomplete recovery after relapses and overall disease progression.^54,55

 

Certain clinical indicators, such as higher relapse rates early in the disease course and MRI characteristics, including total lesion burden and the location of lesions within the CNS, seem to be associated with a higher risk of disease progression.⁵⁶ These are potential prognostic indicators that can help tailor the choice of disease-modifying therapy for patients.⁵⁷ Those with highly inflammatory and potentially aggressive disease at onset, for example, may benefit from early initiation of higher efficacy therapies, whereas those with more benign forms of MS at onset may fare well on lower efficacy therapies. In general, when it comes to currently available MS treatments, higher efficacy is often tied to riskier AE profiles, so the best medication may be the “least efficacious” one that can still control the disease.²⁰

Hauser and colleagues suggested a treatment decision-making model that identifies the interferons, glatiramer acetate, dimethyl fumarate, and teriflunomide as acceptable first-line therapies; fingolimod and natalizumab as acceptable second-line options; and mitoxantrone and alemtuzumab as acceptable third-line therapeutic options.²⁰ The authors generally agree with Hauser and colleagues’ model, and it is important to consider individual patient factors (eg, comorbidities, concurrent medications, life circumstances) and disease severity when deciding on a treatment plan.

Perhaps an even more difficult question is, when is the right time to switch therapies? There remains a dearth of either guidelines or comparative studies for treatment management decisions. Further, without reliable biomarkers, the clinical and pathologic heterogeneity of MS makes treatment difficult.^4,19 In practice, there is general consensus that 1 year of treatment monitoring for effects on clinical and radiologic outcomes is an acceptable time frame to evaluate effectiveness of a disease-modifying treatment. If adherence is maintained and there is still evidence of clinical or MRI activity (suggesting a suboptimal response), an alternative therapy, particularly one with a different MOA, should be strongly considered. This highlights the importance of broad access to all available MS therapies to allow for early selection of a correct therapy that patients will remain adherent to and that controls their disease.

Conclusion

Multiple sclerosis remains a highly unpredictable disease, and relapses have the ability to produce a measurable and sustained impact on the level of disability.⁵⁸ Still, the influence of reduced relapses on preventing disability in an individual patient remains unclear. Large, long-term, prospective cohort studies may clarify whether early treatment affects disease progression and disability.²⁰ However, it is quite evident that effective relapse reduction decreases discomfort, reduces days lost from work and other important activities of daily life, and improves QOL.^58,59

There is still much to learn about this unique disease, but emerging evidence in the medical literature highlights the importance of setting treatment goals that include targeting disease activity to achieve early and effective control. Attaining control with a MS medication seems to be a key component of slowing the physical and emotional disability that can accumulate, helping patients remain active and maintain the highest QOL possible for as long as possible.

Necrotizing soft-tissue infection (NSTI) often is difficult to distinguish from a superficial soft-tissue infection like cellulitis. Both conditions present with pain, edema, and erythema and can be accompanied by fever and malaise. The diagnosis of NSTI must be made quickly because successful treatment requires early surgical debridement and broad-spectrum antibiotics. The following case demonstrates the challenge of diagnosing NSTI.

Case Presentation

A 50-year-old physician developed a sore throat with subjective fevers, night sweats, and chills. After 2 days, his symptoms resolved. The next day he developed right thigh pain while playing tennis and limped off the court. That night he had fevers, chills, and sweats. For the next 3 days, his right thigh pain persisted with waxing and waning fevers.

The patient’s medical history included gastroesophageal reflux disease, vitamin D deficiency, and a positive purified protein derivative test for which he had completed 1 year of isoniazid therapy. The patient was married and in a monogamous relationship with his wife. He had traveled to the Sierra National Forest and Yosemite Park during the preceding winter. He did not swim in a lake or recall a tick bite. He had not consumed raw food, imported meats, or dairy products. He recently started oral fluconazole for tinea corporis.

The patient’s temperature was 39.5°C, heart rate was 115 beats per minute, blood pressure (BP) was 142/88 mm Hg, and respiratory rate was 18 breaths per minute with an oxygen saturation of 95% while breathing ambient air. He was drenched in sweat yet remained comfortable throughout the interview. The oropharyngeal mucosa was moist without lesions or erythema. There was no rash or lymphadenopathy. The lungs were clear to auscultation. The cardiac exam revealed tachycardia. There was point tenderness to deep palpation of the mid-anterior right thigh without crepitus, erythema, or edema.

The patient’s sodium level was 129 mmol/L (normal range 135-145 mmol/L), bicarbonate was 20 mmol/L (normal range 22-32 mmol/L), creatinine was 1.1 mg/dL (normal range 0.7-1.2 mg/dL), and glucose was 194 mg/dL. The white blood cell count (WBC) was 12,900 cells/mm³ (normal range 3,400-10,000 cells/mm³) with 96% neutrophils. The hematocrit was 41% (normal range 41-53%), and the platelet count was 347,000 cells/mm³ (normal range 140,000-450,000 cells/mm³). The lactate level was 2.2 mmol/L (normal range 0-2 mmol/L). The creatine kinase level was 347 U/L (normal range 50-388 U/L), and the lactate dehydrogenase level was 254 U/L (normal range 102-199 U/L). A rapid group A streptococcal (GAS) antigen test was negative. A radiograph of the right femur revealed mildly edematous soft tissue. On ultrasound the right quadriceps appeared mildly edematous, but there was no evidence of abscess or discrete fluid collection (eFigure 1).

eFigure 1. Ultrasound of the Right Anterior Thigh Ultrasound revealed heterogeneous, mildly edematous quadriceps muscle. There was no abscess or discrete fluid collection. There was trace fluid along the fascia of the quadriceps muscle.

Four liters of normal saline, acetaminophen, ceftriaxone, and doxycycline were administered to the patient. Overnight he was afebrile, tachycardic, and normotensive. The following morning his BP decreased to 81/53 mm Hg. His WBC count was 33,000 cells/mm³ with 96% neutrophils. A peripheral blood smear showed immature granulocytes. The sodium and creatinine increased to 135 mmol/L and 1.3 mg/dL, respectively. The erythrocyte sedimentation rate was 20 mm/h (normal range 0-10 mm/h), and the C-reactive protein level was 174 mg/L (normal range < 6.3 mg/L).The right thigh became erythematous and edematous.

Given concern for necrotizing fasciitis, antibiotics were changed to vancomycin, piperacillin-tazobactam, and clindamycin. The patient was taken to the operating room (OR). The right quadriceps muscle was markedly edematous with overlying necrotic fibrofatty tissue with easy separation of the fascia from the anterolateral rectus femoris and rectus lateralis muscles. Necrotizing fasciitis was diagnosed.

The tissue was debrided, and surgical pathology revealed fibroadipose tissue with extensive necrosis and dense acute inflammation (eFigure 2). After the anterolateral space between the fascia and underlying thigh muscle was drained, a Penrose drain was placed, and the wound was left open with plans for a second-look operation within 24 hours.

eFigure 2. Surgical Pathology of Debrided Right Thigh
Pathology revealed fibroadipose tissue with extensive necrosis and dense acute inflammation.

eFigure 3. Right Anterior Thigh
Two Penrose drains inserted after second operation.

In the ensuing hours erythema extended proximal to the operative site. The patient was emergently taken to the OR. The focus of necrotizing fasciitis along the anterolateral aspect of the thigh had extended posteriorly and superiorly. This area was irrigated, all loculations were disrupted, and a second Penrose drain was placed.

 

The wound was left open for 6 more days. On hospital day 9, operative exploration revealed no necrotizing fasciitis. The fascia and skin wound were then closed (eFigure 3).

Cultures from the fascia grew the GAS bacteria Streptococcus pyogenes (S pyogenes), which was sensitive to penicillin. The blood cultures from admission were sterile. A test for Epstein-Barr virus immunoglobulin M antibody was negative. The patient was discharged after 10 days in the hospital to complete a 2-week course of IV penicillin. Two months later he resumed playing tennis and returned to his clinical duties.

Discussion

In the U.S., there are approximately 3.5 cases of invasive GAS infection per 100,000 persons.¹ Type I NSTI is polymicrobial (aerobic and anaerobic organisms). Risk factors include recent surgery, immunocompromised states, drug use, diabetes mellitus, and traumatic wounds.² Type II NSTI is caused by GAS or other β-hemolytic streptococci either alone or in association with another organism, most commonly Staphylococcus aureus. Type II NSTI is classically found on the extremities and occurs in young, healthy, immunocompetent patients—such as this patient.³

The portal of entry in nearly half of type II NSTI is unknown; minor local trauma is often suspected.⁴ However, cases have been reported in which the only identifiable source was a preceding sore throat.⁴ The origin of this patient’s GAS remains unknown, but perhaps his pharyngitis led to transient bacteremia, which then seeded his injured thigh muscle. An in vitro model demonstrated that injured muscles increase surface expression of the cytoskeletal protein vimentin, which binds GAS.⁵ Exotoxins and endotoxins produced by S pyogenes may lead to microvascular thrombosis, tissue ischemia, liquefactive necrosis, and systemic release of cytokines followed by systemic illness, multiorgan dysfunction, and death.⁶

The Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) score was developed to aid in early diagnosis of NSTI.⁷ It was derived from a series of 2,555 patients admitted with cellulitis or abscesses at a single institution. Scores > 8 have a positive predictive value of 93% for NSTI. This patient had a LRINEC score of 9. Radiographs or computed tomography scans may demonstrate soft-tissue air collections but lack sensitivity and are often nondiagnostic.^8,9 T1-weighted magnetic resonance imaging can delineate the anatomic extent of soft-tissue infections but is time consuming and may delay treatment.¹⁰ When the pretest probability is high, proceeding directly to the OR for direct visualization and possible debridement is advisable. Histologic features of necrotizing fasciitis include inflammation with polymorphonuclear cells and necrosis of the subcutaneous fat and fascia with relative sparing of the muscle.¹¹Necrotizing soft-tissue infection requires early surgical debridement and broad-spectrum antibiotic coverage. Without surgical debridement, the mortality rate approaches 100%.² Antibiotics should include activity against Gram-positive, Gram-negative, and anaerobic organisms. The duration of antibiotic therapy has not been defined and is dependent on the patient’s clinical status. Adjunctive treatment options may include IV immunoglobulin and hyperbaric oxygen therapy, although the data supporting their utility are limited.^12,13

Conclusion

Despite the LRINEC scoring systems and advanced imaging, necrotizing fasciitis remains challenging to diagnose in a timely manner. In this case, close monitoring of the patient facilitated timely evaluation and treatment of a fatal disease.

Necrotizing soft-tissue infection (NSTI) often is difficult to distinguish from a superficial soft-tissue infection like cellulitis. Both conditions present with pain, edema, and erythema and can be accompanied by fever and malaise. The diagnosis of NSTI must be made quickly because successful treatment requires early surgical debridement and broad-spectrum antibiotics. The following case demonstrates the challenge of diagnosing NSTI.

Case Presentation

A 50-year-old physician developed a sore throat with subjective fevers, night sweats, and chills. After 2 days, his symptoms resolved. The next day he developed right thigh pain while playing tennis and limped off the court. That night he had fevers, chills, and sweats. For the next 3 days, his right thigh pain persisted with waxing and waning fevers.

The patient’s medical history included gastroesophageal reflux disease, vitamin D deficiency, and a positive purified protein derivative test for which he had completed 1 year of isoniazid therapy. The patient was married and in a monogamous relationship with his wife. He had traveled to the Sierra National Forest and Yosemite Park during the preceding winter. He did not swim in a lake or recall a tick bite. He had not consumed raw food, imported meats, or dairy products. He recently started oral fluconazole for tinea corporis.

The patient’s temperature was 39.5°C, heart rate was 115 beats per minute, blood pressure (BP) was 142/88 mm Hg, and respiratory rate was 18 breaths per minute with an oxygen saturation of 95% while breathing ambient air. He was drenched in sweat yet remained comfortable throughout the interview. The oropharyngeal mucosa was moist without lesions or erythema. There was no rash or lymphadenopathy. The lungs were clear to auscultation. The cardiac exam revealed tachycardia. There was point tenderness to deep palpation of the mid-anterior right thigh without crepitus, erythema, or edema.

The patient’s sodium level was 129 mmol/L (normal range 135-145 mmol/L), bicarbonate was 20 mmol/L (normal range 22-32 mmol/L), creatinine was 1.1 mg/dL (normal range 0.7-1.2 mg/dL), and glucose was 194 mg/dL. The white blood cell count (WBC) was 12,900 cells/mm³ (normal range 3,400-10,000 cells/mm³) with 96% neutrophils. The hematocrit was 41% (normal range 41-53%), and the platelet count was 347,000 cells/mm³ (normal range 140,000-450,000 cells/mm³). The lactate level was 2.2 mmol/L (normal range 0-2 mmol/L). The creatine kinase level was 347 U/L (normal range 50-388 U/L), and the lactate dehydrogenase level was 254 U/L (normal range 102-199 U/L). A rapid group A streptococcal (GAS) antigen test was negative. A radiograph of the right femur revealed mildly edematous soft tissue. On ultrasound the right quadriceps appeared mildly edematous, but there was no evidence of abscess or discrete fluid collection (eFigure 1).

eFigure 1. Ultrasound of the Right Anterior Thigh Ultrasound revealed heterogeneous, mildly edematous quadriceps muscle. There was no abscess or discrete fluid collection. There was trace fluid along the fascia of the quadriceps muscle.

Four liters of normal saline, acetaminophen, ceftriaxone, and doxycycline were administered to the patient. Overnight he was afebrile, tachycardic, and normotensive. The following morning his BP decreased to 81/53 mm Hg. His WBC count was 33,000 cells/mm³ with 96% neutrophils. A peripheral blood smear showed immature granulocytes. The sodium and creatinine increased to 135 mmol/L and 1.3 mg/dL, respectively. The erythrocyte sedimentation rate was 20 mm/h (normal range 0-10 mm/h), and the C-reactive protein level was 174 mg/L (normal range < 6.3 mg/L).The right thigh became erythematous and edematous.

Given concern for necrotizing fasciitis, antibiotics were changed to vancomycin, piperacillin-tazobactam, and clindamycin. The patient was taken to the operating room (OR). The right quadriceps muscle was markedly edematous with overlying necrotic fibrofatty tissue with easy separation of the fascia from the anterolateral rectus femoris and rectus lateralis muscles. Necrotizing fasciitis was diagnosed.

The tissue was debrided, and surgical pathology revealed fibroadipose tissue with extensive necrosis and dense acute inflammation (eFigure 2). After the anterolateral space between the fascia and underlying thigh muscle was drained, a Penrose drain was placed, and the wound was left open with plans for a second-look operation within 24 hours.

eFigure 2. Surgical Pathology of Debrided Right Thigh
Pathology revealed fibroadipose tissue with extensive necrosis and dense acute inflammation.

eFigure 3. Right Anterior Thigh
Two Penrose drains inserted after second operation.

In the ensuing hours erythema extended proximal to the operative site. The patient was emergently taken to the OR. The focus of necrotizing fasciitis along the anterolateral aspect of the thigh had extended posteriorly and superiorly. This area was irrigated, all loculations were disrupted, and a second Penrose drain was placed.

 

The wound was left open for 6 more days. On hospital day 9, operative exploration revealed no necrotizing fasciitis. The fascia and skin wound were then closed (eFigure 3).

Cultures from the fascia grew the GAS bacteria Streptococcus pyogenes (S pyogenes), which was sensitive to penicillin. The blood cultures from admission were sterile. A test for Epstein-Barr virus immunoglobulin M antibody was negative. The patient was discharged after 10 days in the hospital to complete a 2-week course of IV penicillin. Two months later he resumed playing tennis and returned to his clinical duties.

Discussion

In the U.S., there are approximately 3.5 cases of invasive GAS infection per 100,000 persons.¹ Type I NSTI is polymicrobial (aerobic and anaerobic organisms). Risk factors include recent surgery, immunocompromised states, drug use, diabetes mellitus, and traumatic wounds.² Type II NSTI is caused by GAS or other β-hemolytic streptococci either alone or in association with another organism, most commonly Staphylococcus aureus. Type II NSTI is classically found on the extremities and occurs in young, healthy, immunocompetent patients—such as this patient.³

The portal of entry in nearly half of type II NSTI is unknown; minor local trauma is often suspected.⁴ However, cases have been reported in which the only identifiable source was a preceding sore throat.⁴ The origin of this patient’s GAS remains unknown, but perhaps his pharyngitis led to transient bacteremia, which then seeded his injured thigh muscle. An in vitro model demonstrated that injured muscles increase surface expression of the cytoskeletal protein vimentin, which binds GAS.⁵ Exotoxins and endotoxins produced by S pyogenes may lead to microvascular thrombosis, tissue ischemia, liquefactive necrosis, and systemic release of cytokines followed by systemic illness, multiorgan dysfunction, and death.⁶

The Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) score was developed to aid in early diagnosis of NSTI.⁷ It was derived from a series of 2,555 patients admitted with cellulitis or abscesses at a single institution. Scores > 8 have a positive predictive value of 93% for NSTI. This patient had a LRINEC score of 9. Radiographs or computed tomography scans may demonstrate soft-tissue air collections but lack sensitivity and are often nondiagnostic.^8,9 T1-weighted magnetic resonance imaging can delineate the anatomic extent of soft-tissue infections but is time consuming and may delay treatment.¹⁰ When the pretest probability is high, proceeding directly to the OR for direct visualization and possible debridement is advisable. Histologic features of necrotizing fasciitis include inflammation with polymorphonuclear cells and necrosis of the subcutaneous fat and fascia with relative sparing of the muscle.¹¹Necrotizing soft-tissue infection requires early surgical debridement and broad-spectrum antibiotic coverage. Without surgical debridement, the mortality rate approaches 100%.² Antibiotics should include activity against Gram-positive, Gram-negative, and anaerobic organisms. The duration of antibiotic therapy has not been defined and is dependent on the patient’s clinical status. Adjunctive treatment options may include IV immunoglobulin and hyperbaric oxygen therapy, although the data supporting their utility are limited.^12,13

Conclusion

Despite the LRINEC scoring systems and advanced imaging, necrotizing fasciitis remains challenging to diagnose in a timely manner. In this case, close monitoring of the patient facilitated timely evaluation and treatment of a fatal disease.

Necrotizing soft-tissue infection (NSTI) often is difficult to distinguish from a superficial soft-tissue infection like cellulitis. Both conditions present with pain, edema, and erythema and can be accompanied by fever and malaise. The diagnosis of NSTI must be made quickly because successful treatment requires early surgical debridement and broad-spectrum antibiotics. The following case demonstrates the challenge of diagnosing NSTI.

Case Presentation

A 50-year-old physician developed a sore throat with subjective fevers, night sweats, and chills. After 2 days, his symptoms resolved. The next day he developed right thigh pain while playing tennis and limped off the court. That night he had fevers, chills, and sweats. For the next 3 days, his right thigh pain persisted with waxing and waning fevers.

The patient’s medical history included gastroesophageal reflux disease, vitamin D deficiency, and a positive purified protein derivative test for which he had completed 1 year of isoniazid therapy. The patient was married and in a monogamous relationship with his wife. He had traveled to the Sierra National Forest and Yosemite Park during the preceding winter. He did not swim in a lake or recall a tick bite. He had not consumed raw food, imported meats, or dairy products. He recently started oral fluconazole for tinea corporis.

The patient’s temperature was 39.5°C, heart rate was 115 beats per minute, blood pressure (BP) was 142/88 mm Hg, and respiratory rate was 18 breaths per minute with an oxygen saturation of 95% while breathing ambient air. He was drenched in sweat yet remained comfortable throughout the interview. The oropharyngeal mucosa was moist without lesions or erythema. There was no rash or lymphadenopathy. The lungs were clear to auscultation. The cardiac exam revealed tachycardia. There was point tenderness to deep palpation of the mid-anterior right thigh without crepitus, erythema, or edema.

The patient’s sodium level was 129 mmol/L (normal range 135-145 mmol/L), bicarbonate was 20 mmol/L (normal range 22-32 mmol/L), creatinine was 1.1 mg/dL (normal range 0.7-1.2 mg/dL), and glucose was 194 mg/dL. The white blood cell count (WBC) was 12,900 cells/mm³ (normal range 3,400-10,000 cells/mm³) with 96% neutrophils. The hematocrit was 41% (normal range 41-53%), and the platelet count was 347,000 cells/mm³ (normal range 140,000-450,000 cells/mm³). The lactate level was 2.2 mmol/L (normal range 0-2 mmol/L). The creatine kinase level was 347 U/L (normal range 50-388 U/L), and the lactate dehydrogenase level was 254 U/L (normal range 102-199 U/L). A rapid group A streptococcal (GAS) antigen test was negative. A radiograph of the right femur revealed mildly edematous soft tissue. On ultrasound the right quadriceps appeared mildly edematous, but there was no evidence of abscess or discrete fluid collection (eFigure 1).

eFigure 1. Ultrasound of the Right Anterior Thigh Ultrasound revealed heterogeneous, mildly edematous quadriceps muscle. There was no abscess or discrete fluid collection. There was trace fluid along the fascia of the quadriceps muscle.

Four liters of normal saline, acetaminophen, ceftriaxone, and doxycycline were administered to the patient. Overnight he was afebrile, tachycardic, and normotensive. The following morning his BP decreased to 81/53 mm Hg. His WBC count was 33,000 cells/mm³ with 96% neutrophils. A peripheral blood smear showed immature granulocytes. The sodium and creatinine increased to 135 mmol/L and 1.3 mg/dL, respectively. The erythrocyte sedimentation rate was 20 mm/h (normal range 0-10 mm/h), and the C-reactive protein level was 174 mg/L (normal range < 6.3 mg/L).The right thigh became erythematous and edematous.

Given concern for necrotizing fasciitis, antibiotics were changed to vancomycin, piperacillin-tazobactam, and clindamycin. The patient was taken to the operating room (OR). The right quadriceps muscle was markedly edematous with overlying necrotic fibrofatty tissue with easy separation of the fascia from the anterolateral rectus femoris and rectus lateralis muscles. Necrotizing fasciitis was diagnosed.

The tissue was debrided, and surgical pathology revealed fibroadipose tissue with extensive necrosis and dense acute inflammation (eFigure 2). After the anterolateral space between the fascia and underlying thigh muscle was drained, a Penrose drain was placed, and the wound was left open with plans for a second-look operation within 24 hours.

eFigure 2. Surgical Pathology of Debrided Right Thigh
Pathology revealed fibroadipose tissue with extensive necrosis and dense acute inflammation.

eFigure 3. Right Anterior Thigh
Two Penrose drains inserted after second operation.

In the ensuing hours erythema extended proximal to the operative site. The patient was emergently taken to the OR. The focus of necrotizing fasciitis along the anterolateral aspect of the thigh had extended posteriorly and superiorly. This area was irrigated, all loculations were disrupted, and a second Penrose drain was placed.

 

The wound was left open for 6 more days. On hospital day 9, operative exploration revealed no necrotizing fasciitis. The fascia and skin wound were then closed (eFigure 3).

Cultures from the fascia grew the GAS bacteria Streptococcus pyogenes (S pyogenes), which was sensitive to penicillin. The blood cultures from admission were sterile. A test for Epstein-Barr virus immunoglobulin M antibody was negative. The patient was discharged after 10 days in the hospital to complete a 2-week course of IV penicillin. Two months later he resumed playing tennis and returned to his clinical duties.

Discussion

In the U.S., there are approximately 3.5 cases of invasive GAS infection per 100,000 persons.¹ Type I NSTI is polymicrobial (aerobic and anaerobic organisms). Risk factors include recent surgery, immunocompromised states, drug use, diabetes mellitus, and traumatic wounds.² Type II NSTI is caused by GAS or other β-hemolytic streptococci either alone or in association with another organism, most commonly Staphylococcus aureus. Type II NSTI is classically found on the extremities and occurs in young, healthy, immunocompetent patients—such as this patient.³

The portal of entry in nearly half of type II NSTI is unknown; minor local trauma is often suspected.⁴ However, cases have been reported in which the only identifiable source was a preceding sore throat.⁴ The origin of this patient’s GAS remains unknown, but perhaps his pharyngitis led to transient bacteremia, which then seeded his injured thigh muscle. An in vitro model demonstrated that injured muscles increase surface expression of the cytoskeletal protein vimentin, which binds GAS.⁵ Exotoxins and endotoxins produced by S pyogenes may lead to microvascular thrombosis, tissue ischemia, liquefactive necrosis, and systemic release of cytokines followed by systemic illness, multiorgan dysfunction, and death.⁶

The Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) score was developed to aid in early diagnosis of NSTI.⁷ It was derived from a series of 2,555 patients admitted with cellulitis or abscesses at a single institution. Scores > 8 have a positive predictive value of 93% for NSTI. This patient had a LRINEC score of 9. Radiographs or computed tomography scans may demonstrate soft-tissue air collections but lack sensitivity and are often nondiagnostic.^8,9 T1-weighted magnetic resonance imaging can delineate the anatomic extent of soft-tissue infections but is time consuming and may delay treatment.¹⁰ When the pretest probability is high, proceeding directly to the OR for direct visualization and possible debridement is advisable. Histologic features of necrotizing fasciitis include inflammation with polymorphonuclear cells and necrosis of the subcutaneous fat and fascia with relative sparing of the muscle.¹¹Necrotizing soft-tissue infection requires early surgical debridement and broad-spectrum antibiotic coverage. Without surgical debridement, the mortality rate approaches 100%.² Antibiotics should include activity against Gram-positive, Gram-negative, and anaerobic organisms. The duration of antibiotic therapy has not been defined and is dependent on the patient’s clinical status. Adjunctive treatment options may include IV immunoglobulin and hyperbaric oxygen therapy, although the data supporting their utility are limited.^12,13

Conclusion

Despite the LRINEC scoring systems and advanced imaging, necrotizing fasciitis remains challenging to diagnose in a timely manner. In this case, close monitoring of the patient facilitated timely evaluation and treatment of a fatal disease.