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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Crit Care Med. Author manuscript; available in PMC 2017 May 1.
Published in final edited form as:
PMCID: PMC4833588
NIHMSID: NIHMS753357

Functional Status in ICU Survivors and out of hospital outcomes: a cohort study

Abstract

Objective

Functional status at hospital discharge may be a risk factor for adverse events among survivors of critical illness. We sought to examine the association between functional status at hospital discharge in survivors of critical care and risk of 90-day all-cause mortality after hospital discharge.

Design

Single center retrospective cohort study

Setting

Academic Medical Center

Patients

10,343 adults who received critical care from 1997 to 2011 and survived hospitalization.

Interventions

None

Measurements and Main Results

The exposure of interest was functional status determined at hospital discharge by a licensed physical therapist and rated based on qualitative categories adapted from the Functional Independence Measure. The main outcome was 90-day post hospital discharge all-cause mortality. A categorical risk prediction score was derived and validated based on a logistic regression model of the function grades for each assessment. In an adjusted logistic regression model, the lowest quartile of functional status at hospital discharge was associated with an increased odds of 90-day post-discharge mortality compared to patients with independent functional status [OR=7.63 (95%CI 3.83, 15.22; P<0.001)]. In patients who had at least seven days of physical therapy treatment prior to hospital discharge (N=2,293), the adjusted odds of 90-day post-discharge mortality in patients with marked improvement in functional status at discharge was 64% less than patients with no change in functional status [OR 0.36 (95%CI 0.24–0.53); P<0.001].

Conclusions

Lower functional status at hospital discharge in survivors of critical illness is associated with increased post-discharge mortality. Further, patients whose functional status improves before discharge have decreased odds of post-discharge mortality.

Introduction

Critical illness is an important public health issues because of the high rate of mortality and sizeable healthcare costs. In 2008–2009, there were 4.14 million patients admitted to intensive care representing a substantial increase over the prior decade (13). In-hospital mortality is 12% for patients who receive critical care and can be as high as 30% in those with sepsis (4). As critical illness in-hospital mortality has decreased during the last 30 years, interest in long-term outcomes of intensive care unit (ICU) survivors has deepened. ICU survivors have a high mortality rate and often suffer long-term physical impairments, profound neuromuscular weakness, exercise limitation, neuropsychological issues, increased healthcare utilization and lower quality of life following hospital discharge (512).

Early physical therapy in the ICU has historical precedence (13, 14) and has been shown to be safe (1517). Patients on mechanical ventilation who receive early physical therapy have improved functional independence (15, 16). Early implementation of a combination of sedation interruption, physical therapy and occupational therapy interventions is shown to improve functional status at hospital discharge (17). A recent systematic review and meta-analysis noted that physical therapy in the ICU may be related to improved quality of life, better physical function, more ventilator-free days, and shorter hospital and ICU length of stay (18). However, despite the benefits, studies show that physical therapy interventions are historically underutilized in the ICU (16, 19).

While studies suggest that functional status may be modifiable in the ICU (16, 17, 2022), to date, limited information exists in ICU survivors regarding the association between functional status at hospital discharge and adverse events following hospital discharge. We hypothesized that poor functional status at hospital discharge would be associated with increased mortality during the 90 days following hospital discharge. To explore this hypothesis, we performed a single center observational cohort study of 10,343 critically ill adults from 1997 to 2011 who had an evaluation by a physical therapist at hospital discharge. Further, we investigated whether improvement in functional status during hospitalization is associated with improved 90-day mortality following hospital discharge.

Materials and Methods

Source population and Data Sources

We abstracted patient-level administrative and laboratory data from the Brigham and Women’s Hospital (BWH), a 793 bed teaching hospital in Boston, Massachusetts. Data on all patients admitted to BWH between November 20, 1997 and April 5, 2011 were obtained through the Brigham Integrated Computing System (BICS) (23) and the Research Patient Data Registry (RPDR) at Partners HealthCare (24, 25). Approval for the study was granted by the Partners Human Research Committee (Institutional Review Board).

Study population

Patients were eligible for study inclusion if they were adults admitted to the Brigham and Women’s Hospital as inpatients and received medical or surgical intensive care during their hospitalization. During the study period, there were 37,271 individual patients, age ≥18 years, who were assigned the Current Procedural Terminology code 99291 (critical care, first 30–74 minutes) during hospitalization admission. We have previously validated the Current Procedural Terminology code 99291 for ICU admission in the RPDR database (26). Exclusions included: 118 foreign patients without Social Security Numbers; 1,587 patients assigned CPT code 99291 who received care only in the Emergency Room, were not admitted and were not assigned a Diagnosis Related Group (DRG); 4,528 patients who died as in-patients; and 20,695 patients who did not receive a formal structured evaluation from a Physical Therapist within 48 hours of hospital discharge. Thus, the analytic cohort was comprised of 10,343 patients whom were evaluated by a Physical Therapist within 48 hours of hospital discharge. The derivation cohort consisted of a random selection of 2/3 of the analytic cohort (n= 6,895), and the validation cohort comprised the remaining 1/3 of the analytic cohort (n= 3,448) (Figure 1).

Figure 1
Flow Chart

Exposure of interest and covariates

The exposure of interest was functional status at hospital discharge defined as physical function assessed at the time of hospital discharge. Data was obtained from licensed physical therapists trained on the determination of physical function based on qualitative categories adapted from the functional mobility sub scales of the Functional Independence Measure (FIM) (27, 28). The FIM mobility sub scales incorporate transfers (including bed, chair, and wheelchair) as well as locomotion (including walking/wheelchair and stairs), and are scored on an ordinal scale based on percentage of active patient participation in the selected task (27). The adapted scoring system grades patients on a scale of function with six designations from independent through dependent for motor tasks assessed, with a determination of not applicable used when a patient was either incapable of progressing to the designated task or for physical or medical limitations. The six designations were independent, standby assist/supervision, minimal assist, moderate assist, maximal assist, and total assist (Supplemental Table 1). Patients were assessed on bed mobility (roll side to side, supine to sit, sit to supine), transfers (sit to stand, stand to sit, bed to chair), and gait (level ambulation, stairs). The FIM scoring system is widely used by rehabilitation practitioners across the continuum of care and allows for clinicians to follow up and compare functional status throughout the rehabilitation process (29). The physical therapists were not aware of the study hypothesis, exposure or outcomes.

Race was either self-determined or designated by a patient representative/healthcare proxy. Patient admission ‘type’ was defined as ‘medical’ or ‘surgical’ and incorporates the DRG methodology (30). We utilized the Deyo-Charlson Index to assess the burden of chronic illness which is well studied and validated (31). Sepsis is defined by the presence of any of the following ICD-9-CM codes: 038.0–038.9, 790.7, 117.9, 112.5, or 112.81, three days prior to critical care initiation to seven days after critical care initiation, an approach that we have validated in our database (32). Using electronic pharmacy records, exposure to inotropes and vasopressors was determined for dopamine, dobutamine, epinephrine, norepinephrine, phenylephrine, milrinone and vasopressin. Inotropes or vasopressors were considered to be present if prescribed three days prior to critical care initiation to seven days after critical care initiation (33). Exposure to high dose intravenous glucocorticoids following ICU admission was determined for hydrocortisone, methylprednisolone and dexamethasone and were considered to be present if prescribed for at least four doses following ICU admission (34). Exposure to neuromuscular blocking agents (NMBA) following ICU admission was determined for cisatracurium, mivacurium, pancuronium, vecuronium, rocuronium and considered to be present if prescribed for at least four doses following ICU admission (35).

Number of organs with acute failure was adapted from Martin et.al. (36) and defined by a combination of ICD-9-CM and CPT codes relating to acute organ dysfunction (respiratory, cardiovascular, renal, hepatic, hematologic, metabolic and or neurologic) assigned from 3 days prior to critical care initiation to 30 days after critical care initiation (26, 32, 33). Acute kidney injury was defined as RIFLE class Injury or Failure occurring between three days prior to critical care initiation and seven days after critical care initiation (37). Noncardiogenic acute respiratory failure was identified by the presence of ICD-9 codes for respiratory failure or pulmonary edema (518.4, 518.5, 518.81, and 518.82) and mechanical ventilation (96.7×), excluding congestive heart failure (428.0–428.9) following hospital admission (38). For severity of illness risk adjustment, we employed the Acute Organ Failure score, an ICU risk-prediction score derived and validated from demographics (age, race), patient admission ‘type’ as well as ICD-9-CM code based comorbidity, sepsis and number of organs with acute failure covariates which has similar discrimination for 30 day mortality as APACHE II (39). Malnutrition was considered to be present if the patient was diagnosed by a Registered Dietitian 10 days prior to 2 days after ICU admission with nonspecific protein-calorie malnutrition; or specific (mild, moderate or severe) protein-calorie malnutrition (40, 41).

End points

The primary end point was 90-day all-cause mortality following hospital discharge (33). Secondary endpoints included 30 and 365-day all-cause mortality following hospital discharge.

Assessment of Mortality

Vital status was obtained from the Social Security Administration Death Master File which has high sensitivity and specificity for mortality (42). We have validated the accuracy of the Social Security Administration Death Master File for in-hospital and out-of-hospital mortality in the RPDR database (26). 100% of the cohort had at least 90-day follow up after hospital discharge. The censoring date was April 6, 2012.

Power calculations

Previously, in a cohort of critically ill patients (n=43,212), we studied post-discharge mortality in ICU survivors (33). From these data, we assumed that 90-day post-discharge mortality would be 4% higher among the patients with the lowest quartile function compared to those the highest quartile function. With an alpha error level of 5% and a power of 80%, the sample size required for our primary end point (90-day post-discharge mortality) was 852 patients with the lowest quartile of functional status and 852 patients with independent functional status.

Derivation and validation of the risk score

A clinical prediction model was created based on a logistic regression model describing the risk of 90-day post-discharge mortality as a function of the predictors at discharge [Bed Mobility (roll side to side, supine to sit and sit to supine), Transfers (sit to stand, stand to sit and bed to chair) and Gait level]. We utilized a random number service that generates randomness via atmospheric noise to randomly select of 2/3 of the analytic cohort for the derivation cohort and 1/3 of the analytic cohort for the validation cohort (43). The model was transformed to a simplified integer-based score, with a score for the scale of function for each individual predictor variable [i.e. “independent” for Bed Mobility (roll side to side)] assigned by dividing its β-coefficient by the smallest coefficient in the model, multiplying by 2 and rounding up to the nearest integer (Supplemental Table 2). A risk score was then calculated for each patient by summing each individual predictor variable score. Subsequently, the population was first divided into quartiles of risk score with the highest quartile of functional status then subdivided into independent and patients at low risk. Thus the five categories assigned were: independent patients, patients at low risk, patients at intermediate risk, patients at high risk and patients at very high risk for death.

Descriptive statistics

Categorical variables were described by frequency distribution, and compared across outcome groups using contingency tables and chi-square testing. Continuous variables were examined graphically and in terms of summary statistics, and then compared across outcome groups using one-way analysis of variance or the Kruskal–Wallis test. For the 90-day post-discharge mortality model, specification of each continuous covariate (as a linear versus categorical term) was adjudicated by the empiric association with the primary outcome using Akaike’s Information Criterion. Adjusted odds ratios were estimated by multivariable logistic regression models with inclusion of covariate terms thought to plausibly associate with both functional status and 90-day post-discharge mortality. We individually tested for effect modification by functional status by year of hospital admission, hospital length of stay, neuromuscular blocking agent use, nutritional status, vasopressors/ inotropes, glucocorticoid use and metastatic malignancy by adding an interaction term to the multivariate models. Further, a multivariable Cox's proportional hazards model was used to illustrate post-discharge survival related to functional status. For the time to mortality, we estimated the survival curves according to functional status quartile with the Kaplan-Meier method and compared the results via the log-rank test.

Performance of the score

The discriminatory ability of the clinical predication model for 90-day post-discharge mortality was quantified using the c-statistic. Calibration was assessed using the Hosmer-Lemeshow χ2 goodness-of-fit test and the accompanying p-value, based on ten subgroups of participants and 8 degrees of freedom (44). Bayes information criterion was also used to determine global model fit (45). The improvement in model performance was evaluated via net reclassification improvement with a cut-point of 50% or integrated discrimination improvement (46).

Subanalyses

In patients who had a functional status evaluation at least seven days prior to the discharge functional status evaluation we evaluated the association between improved functional status and post-discharge mortality. The change in physical therapy score between initial and discharge evaluation was categorized in five groups a priori based on risk score points distribution noted in the FIM mobility sub scales (specifically Transfers and Bed Mobility) between independent, minimal assist and maximal assist scale of function (Supplemental Table 2).

In all analyses, p-values are two-tailed and values below 0.05 were considered statistically significant. All analyses were performed using STATA 13.1MP statistical software (StataCorp LP, College Station, TX).

Results

Survival Analysis and Risk-Scoring System

Significant differences exist between the derivation cohort and the excluded 20,695 patients who did not receive a formal structured evaluation from a Physical Therapist (Supplemental Table 3). Patient characteristics of the derivation cohort were stratified according to 90-day post-discharge mortality (Table 1). The mean age at hospital admission was 63.6 years. Most patients were male (54%), white (78%), had a surgically-related DRG (61%). Derivation cohort patients were discharged to a care facility (56%), to home care (27%), to home without services (10%) or to hospice (0.4%). Factors that were associated with 90-day post-discharge mortality included higher age, medical patient type, higher Deyo-Charlson Index, increased number of organs with acute failure. In addition, 90-day post-discharge mortality was heightened with the presence of malignant neoplasm, malnutrition, acute kidney injury, or sepsis, as well as an increased hospital length of stay and a higher Acute Organ Failure score (Table 1). 30, 90 and 365-day post-discharge mortality rates were 4.5%, 9.8%, and 20.6%, respectively.

Table 1
Population Characteristics of the Derivation Cohort and Unadjusted Association of Potential Prognostic Determinants With 90-Day Post Discharge Mortalitya

The patients were divided into five groups on the basis of the functional status risk score point distribution, which ranged from 0 to 29 points. Survival estimates for the five groups showed different post-discharge mortality rates at 90 and 365 days: independent (0 points), low risk (1 to 8 points), intermediate risk (9 to 18 points), high risk (19 to 24 points) and very high risk (>24 points) (Table 2).

Table 2
Risk of Death at 30 days, 90 days and 1 Year post discharge in the Development and Validation Cohorts, According to Risk Category.*

Small but statistically significant differences in Deyo-Charlson index are present between derivation and validation cohorts (Supplemental Table 4). Classification of the derivation cohort according to risk score resulted in similar percentages of patients assigned to risk groups as in the derivation cohort (Table 2). Further the 90-day post-discharge mortality rates for the independent, low, intermediate, high and very high risk groups were similar in the derivation and validation cohorts (Table 3). The AUC for the prediction model for 90 day post-discharge mortality was 0.673 (95%CI 0.63–0.77) in the derivation cohort and 0.674 (95%CI 0.65–0.70) in the validation cohort. The prediction model showed good calibration in the derivation and validation cohorts (HL χ2 13.3, P= 0.10 and HL χ2 11.1, P= 0.20 respectively). Thus, the risk score showed good calibration and similar discrimination in the derivation and validation cohorts.

Table 3
Patient characteristics of validation cohort by risk category group

Primary Outcome

In the validation cohort, mortality in the 90 days after hospital discharge was higher in patients with decreased functional status at hospital discharge. The Kaplan-Meier plot (Figure 2) demonstrates survival grouped according to functional status at hospital discharge in the cohort and shows a significant difference between the five curves (P< 0.001). The odds of 90-day post-discharge mortality in patients with high risk and very high risk functional status at discharge were 7.1 and 10.6 fold higher respectively than patients with independent functional status (Table 4). Functional status remained a significant predictor of the odds of 90-day post-discharge mortality after adjustment for the Acute Organ Failure score and gender. The adjusted odds of 90-day post-discharge mortality in patients with high risk and very high risk functional status at discharge were 5.4 and 7.6 fold higher respectively than patients with independent functional status (Table 4). The adjusted 90-day post-discharge mortality model showed good calibration (HL chi-squared 9.62, P = 0.29) and discrimination [c-statistic= 0.73 (95%CI 0.70–0.75)]. Further, the hazard ratio of mortality adjusted for the Acute Organ Failure score and gender in patients with high risk and very high risk functional status at discharge were 2.09 (95% CI 1.65–2.66) and 2.19 (95% CI 1.73–2.78) respectively relative to patients with independent functional status.

Figure 2
Time-to-Event curves for post discharge mortality in validation cohort (N=3,448)
Table 4
Unadjusted and adjusted associations between functional status risk category and 90-day post-discharge mortality (N= 3,448)

In the validation cohort, there is no significant effect modification of the functional status-90-day post-discharge mortality association on the basis of year of hospital admission (P-interaction=0.33), hospital length of stay (P-interaction=0.21), neuromuscular blocking agent use (P-interaction =0.89), glucocorticoid use (P-interaction=0.70) or nutritional status (P-interaction =0.27). Effect modification is present regarding the presence of vasopressors/inotropes (P-interaction=0.01), and metastatic malignancy (P-interaction<0.001). Individually adding a year, vasopressor/inotrope, neuromuscular blocking agent, glucocorticoid use or a metastatic malignancy term to the final model does not alter the effect size or significance of the change in functional status-90 day post-discharge mortality association (data not shown). Evaluation of the functional status-90 day post-discharge mortality association prior to and after 2005 showed similar estimates (Supplemental Table 5). While patients with and without vasopressor/inotrope or metastatic malignancy present have different risk estimates, the directionality and significance of the functional status-post-discharge mortality association remains.

Secondary Outcome

We evaluated the net reclassification improvement and integrated discrimination improvement following inclusion of functional status in a multivariable adjusted model. A model adjusted for Acute Organ Failure score and gender showed good calibration (HL χ2 0.87, P= 0.99) and discrimination for 90-day post-discharge mortality [AUC 0.70 (95%CI 0.67–0.73)]. The model adjusted for Acute Organ Failure score, gender, and functional status risk score showed good calibration and improved discrimination [AUC 0.73 (Table 3)]. Differences in model discrimination between the models were significant (χ2 18.34, P<0.001). Further, with inclusion of functional status risk score to the model adjusted for Acute Organ Failure score and gender, the net reclassification improvement (NRI) was estimated at 0.039 (P=0.016) and the integrated discrimination improvement (IDI) was estimated at 0.024 (P>0.001). The NRI and IDI suggest that including functional status risk score results in a small but significant improvement in model performance.

Subanalysis

In patients who had a functional status evaluation at least seven days prior to the discharge functional status evaluation we evaluated the association between improved functional status and post-discharge mortality. The change in physical therapy score between initial and discharge evaluation was categorized in five groups: Marked improvement (>9 points down), Intermediate improvement (4 to 9 points down), Modest improvement (1 to 3 points down), No improvement (0 points) and Worse functional status (increase in points). Differences were present among the change in functional status groups (Supplemental Table 6).

In a subset of analytic cohort patients who had functional status assessed at least seven days prior to discharge and at discharge (N=2,293), we analyzed the association between change in functional status and 90-day post-discharge mortality. Mortality 90 days following hospital discharge was lower in patients whose functional status improved prior to hospital discharge. The odds of 90-day post-discharge mortality in patients with marked or intermediate improvement in functional status at discharge was 64% and 50% respectively less than patients with no change in functional status after adjustment for the Acute Organ Failure score, gender and the number of days between initial and discharge physical therapy assessment (Table 5). When change in functional status was a modeled as a continuous exposure, the odds of adjusted 90 day post-discharge mortality showed a relative decrease of 6% for every 1 unit decrease in functional status risk score [OR 0.94 (95%CI 0.92–0.96; P<0.001)].

Table 5
Risk of Death at 90 days and 1 Year after Discharge According to Functional Status Improvement. (N= 2,293)

Discussion

In our cohort of adult survivors of critical care, we sought to characterize the relationship between functional status at hospital discharge and subsequent mortality. Our data suggests that there is a heightened risk of 90-day post-discharge mortality in ICU survivors with decreased functional status. Our work highlights the importance of functional status in risk assessment and suggests that functional status may be a modifiable risk factor for outcomes of ICU survivors.

In ICU survivors, long-term functional impairment is a common complication (47). Patients with pre-ICU functional disability have heightened mortality in the year following ICU admission (48). Hospitalization is associated with decline in functional status and independence (49, 50). Skeletal muscle atrophy can be demonstrated with more than 72 hours of immobilization in healthy subjects (51). With prolonged bed rest, older adults show larger losses of muscle mass and strength relative to young adults (52). In the critically ill, muscle mass loss and decreased strength are common complications (5356). Pre-ICU functional disability in basic, instrumental, and mobility activities is associated with a heightened mortality in the year following ICU admission (48). Further, self-perceived functional status of ICU survivors 6 and 12 months following hospital discharge is associated with self-perceived basal functional status determined at ICU admission (57). Additionally, frailty is an important driver of ICU survivorship and out of hospital outcomes (54, 58) and likely influences functional status at hospital discharge. Frailty closely correlates with ageing (59) and functional status changes due to critical illness likely differ by age (48).

ICU-acquired weakness (60) is associated with multiple organ failure, hyperglycemia, corticosteroids, neuromuscular blockers, decreased nutrient intake, malignancy, muscle wasting from catabolism, physical inactivity and immobilization (6167). Decreased measured strength in critically ill patients is shown to be associated with adverse in-hospital outcomes (68). Importantly, early rehabilitation in the ICU is demonstrated to be safe and can improve functional status (1517, 22). In critical illness survivors, exercise training during hospitalization improved functional capacity and muscle force measured at hospital discharge (22).

The potential limitations of this study are related to the observational design with inherent biases related to confounding, potential reverse causation, and the lack of a randomly-distributed exposure. Ascertainment bias may be present as the study cohort had functional status measured for reasons that may be absent in other ICU patients. The study was performed in a single Boston tertiary care hospital and thus the results may not be generalizable to other acute care settings. Residual confounding may be present despite adjustment for multiple potential confounders. While the established scales of functional status were utilized (27, 28), these measures are subjective and potentially subject to misclassification. We are also unable to adjust for some variables that can alter functional status, including immobility and catabolism. Further, we do not have objective measures of sacropenia (69) or electrophysiological features of possible critical illness polyneuropathy or critical illness myopathy (70).

The present study has several strengths and is unique in that it incorporates functional status directly measured by a physical therapy practitioner to investigate 90-day post-discharge mortality in ICU survivors. Long term post-discharge mortality is previously validated in the RPDR database under study (26). We have sufficient statistical power to detect a clinically relevant difference in 90-day post-discharge mortality. In addition, we utilized validated assessments of ICU admission (26), comorbidities (37), severity of illness (39), acute kidney injury (37) and sepsis (32).

Conclusions

In this single center study of 10,343 hospitalized patients, we demonstrate that decreased functional status at hospital discharge is associated with increased mortality following discharge. Further, an improvement in functional status prior to hospital discharge is associated with improved post-discharge mortality. Though our study cannot determine causation, our clinical data linking improved functional status with better clinical outcomes supports the rationale for physical therapy in ICU survivors before hospital discharge.

Our data supports that the performance of functional status evaluation at discharge can identify ICU survivors at high risk for subsequent adverse events. Early performance of functional status evaluation in combination with screening for frailty following ICU admission may identify patients who are at highest risk for functional decline and those who may most benefit from physical therapy. As ICU survivors transition to the outpatient setting, it is important to screen for impairment of physical function in addition to cognitive function and nutritional status, and align advance care planning appropriately. The emphasis of strength maintenance or improvement during hospitalization and following hospital discharge should be part of a multidisciplinary effort to maximize the potential for recovery in ICU survivors.

Supplementary Material

Acute Organ Failure Score Manuscript

Supplemental Data File _.doc_ .tif_ pdf_ etc._

Acknowledgments

This manuscript is dedicated to the memory of our dear friend and colleague Nathan Edward Hellman, MD, PhD. The authors thank Shawn Murphy and Henry Chueh and the Partners HealthCare Research Patient Data Registry group for facilitating use of their database.

Funding: None

Dr. Mogensen received honoraria for speaking for various subunits of the Massachusetts Dietetic Association as well as the Academy of Nutrition and Dietetics. He received funding from ThriveRx (Receives consulting fees for participating in the ThriveRx Nutrition Advisory Board. The function of the Board is to review policies and procedures and other clinical concerns of the company), Wolf Rinke Associates, Inc. (Provides continuing professional education primarily for registered dietitians. Dr. Mogensen receives royalties as well as one-time payments for educational modules that he has written), and from The American Society for Parenteral and Enteral Nutrition (Dr. Mogensen has received honoraria for speaking at Clinical Nutrition Week as well as speaking or moderating various Webinars). Dr. Quraishi received funding from the National Institutes of Health (NIH) (L30 TR001257) and from Lungpacer, Inc. He disclosed that his wife, Ayesha N. Khalid, MD, MBA, receives salary from Doctella, Inc. His institution received grant support from the NIH.

Footnotes

Institution where work was performed: The Nathan E. Hellman Memorial Laboratory, Renal Division, Brigham and Women's Hospital, Boston, MA

Author Contributions: Conception and design: JER, CMH, KA, KBC; Analysis and interpretation: KBC; Drafting the manuscript for important intellectual content: JER, CMH, KMM, SAQ, KA, KBC

.

Copyright form disclosures: The remaining authors have disclosed that they do not have any potential conflicts of interest.

Contributor Information

Jessica E. Rydingsward, Department of Rehabilitation, Brigham and Women’s Hospital.

Clare M. Horkan, Department of Medicine, Brigham and Women's Hospital.

Kris M. Mogensen, Department of Nutrition, Brigham and Women's Hospital.

Sadeq A. Quraishi, Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital.

Karin Amrein, Division of Endocrinology and Metabolism, Department of Internal Medicine, Medical University of Graz, Austria.

Kenneth B. Christopher, The Nathan E. Hellman Memorial Laboratory, Renal Division, Department of Medicine, Brigham and Women's Hospital.

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