OriginalInvestigation | PhysicalMedicineandRehabilitation ...

Original Investigation | Physical Medicine and Rehabilitation

Assessment of Patient Ambulation Profiles to Predict Hospital Readmission,Discharge Location, and Length of Stay in a Cardiac Surgery Progressive Care UnitIn cheol Jeong, PhD; Ryan Healy, MS; Benjamin Bao; William Xie; Tim Madeira, NP; Marc Sussman, MD; Glenn Whitman, MD; Jennifer Schrack, PhD; Nicole Zahradka, PhD;Erik Hoyer, MD; Charles Brown, MD; Peter C. Searson, PhD

Abstract

IMPORTANCE Promoting patient mobility during hospitalization is associated with improvedoutcomes and reduced risk of hospitalization-associated functional decline. Therefore, accuratemeasurement of mobility with high–information content data may be key to improved risk predictionmodels, identification of at-risk patients, and the development of interventions to improveoutcomes. Remote monitoring enables measurement of multiple ambulation metrics incorporatingboth distance and speed.

OBJECTIVE To evaluate novel ambulation metrics in predicting 30-day readmission rates, dischargelocation, and length of stay using a real-time location system to continuously monitor the voluntaryambulations of postoperative cardiac surgery patients.

DESIGN, SETTING, AND PARTICIPANTS This prognostic cohort study of the mobility of 100patients after cardiac surgery in a progressive care unit at Johns Hopkins Hospital was performedusing a real-time location system. Enrollment occurred between August 29, 2016, and April 4, 2018.Data analysis was performed from June 2018 to December 2019.

MAIN OUTCOMES AND MEASURES Outcome measures included 30-day readmission, dischargelocation, and length of stay. Digital records of all voluntary ambulations were created where eachambulation consisted of multiple segments defined by distance and speed. Ambulation profilesconsisted of 19 parameters derived from the digital ambulation records.

RESULTS A total of 100 patients (81 men [81%]; mean [SD] age, 63.1 [11.6] years) were evaluated.Distance and speed were recorded for more than 14 000 segments in 840 voluntary ambulations,corresponding to a total of 127.8 km (79.4 miles) using a real-time location system. Patientambulation profiles were predictive of 30-day readmission (sensitivity, 86.7%; specificity, 88.2%; Cstatistic, 0.925 [95% CI, 0.836-1.000]), discharge to acute rehabilitation (sensitivity, 84.6%;specificity, 86.4%; C statistic, 0.930 [95% CI, 0.855-1.000]), and length of stay (correlationcoefficient, 0.927).

CONCLUSIONS AND RELEVANCE Remote monitoring provides a high–information contentdescription of mobility, incorporating elements of step count (ambulation distance and relatedparameters), gait speed (ambulation speed and related parameters), frequency of ambulation, andchanges in parameters on successive ambulations. Ambulation profiles incorporating multipleaspects of mobility enables accurate prediction of clinically relevant outcomes.

JAMA Network Open. 2020;3(3):e201074. doi:10.1001/jamanetworkopen.2020.1074

Key PointsQuestion Are patient ambulation

profiles predictive of hospital

readmission, discharge location, and

length of stay?

Findings In this prognostic cohort study

of 100 adults in a cardiac surgery

progressive care unit, patient

ambulation profiles were predictive of

30-day readmission (C statistic, 0.925),

discharge location (C statistic, 0.930),

and length of stay (correlation

coefficient, 0.927).

Meaning Patient ambulation profiles

from a real-time location system enable

prediction of clinically relevant

outcomes.

+ Supplemental content

Author affiliations and article information arelisted at the end of this article.

Open Access. This is an open access article distributed under the terms of the CC-BY License.

JAMA Network Open. 2020;3(3):e201074. doi:10.1001/jamanetworkopen.2020.1074 (Reprinted) March 17, 2020 1/11

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Introduction

Promoting patient mobility is recognized as an important strategy to decrease the risk ofhospitalization-associated functional decline and to improve postoperative recovery.1-4 However,accurate measurement of mobility over time in the hospital remains a major roadblock to developingaccurate risk prediction models, identifying at-risk patients, and developing interventions to improveoutcomes.5

Although there are many methods to assess mobility, each has advantages and limitations.Mobility questionnaires provide a snapshot of a patient’s functional status but are subjective anddifficult to measure continuously. Timed-walk tests provide information on distance and speed6,7 butare labor intensive and, hence, are impractical to implement multiple times during hospitalization.Nonetheless, studies8,9 in different patient populations using these methods have highlighted thepotential in predicting outcomes such as length of stay. Wearable accelerometer-based sensorsenable continuous monitoring of patient ambulation,10 although validation and standardization inindividuals who walk slowly and/or with abnormal gait patterns remains a challenge.11-15 Despitethese limitations, studies10,16,17 of hospitalized patients with wearable accelerometers have foundstatistically significant differences in patient outcomes (eg, length of stay, 30-day readmission, andpatient disposition) based on daily step counts. However, estimations of outcomes are modest atbest, with areas under the curve of approximately 0.7. Although these studies have largely focusedon step count, it is not known which ambulation metrics are most predictive of patient outcomes.Although daily step counts enable continuous monitoring, additional parameters, such as ambulationspeed and the change in distance and speed during recovery, could refine assessment of patientmobility and improve prediction models. The objectives of this study were to use a real-time locationsystem to continuously monitor voluntary out-of-room ambulations of postoperative cardiac surgerypatients and to assess multiple ambulation metrics in predicting readmission rate, discharge location,and length of stay.

Methods

Study Design and ParticipantsThis prognostic cohort study was approved by the Johns Hopkins institutional review board as aresearch project, and all patients provided written informed consent. This study followsStrengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelinefor cohort studies and the Transparent Reporting of a Multivariable Prediction Model for IndividualPrognosis or Diagnosis (TRIPOD) reporting guideline.

The study was performed in a 33-bed cardiac surgery progressive care unit (PCU) at JohnsHopkins Hospital with patients undergoing surgery between August 29, 2016, and April 4, 2018(Table; eAppendix 1 in the Supplement). Eligibility was determined through review of the electronichealth record.

Ambulation ProfilesPatient out-of-room ambulations were measured using an infrared (IR) real-time location system(RTLS; Midmark) designed for location of staff and equipment. An IR badge worn by an individualemits an IR signal that is detected by ceiling sensors (Figure 1A). The sensors are located atapproximately 6-m (20-ft) intervals in the corridors, and the diameter of the detection zone isapproximately 3.5 m (11 ft).18 Detection of a badge signal by a ceiling sensor results in transmission ofthe badge identification number, sensor location, and a time stamp to a server. We have previouslyvalidated the system for ambulation monitoring in a timed-walk test.18

The IR badges were attached to the patient’s gown after leaving the operating room or on thefirst postoperative day. From the badge data for each patient, ambulation maps were assembledfrom the individual segments corresponding to detection by successive ceiling sensors. We defined

JAMA Network Open | Physical Medicine and Rehabilitation Patient Ambulation Profiles, Hospital Readmission, Discharge Location, and Length of Stay



http://www.equator-network.org/reporting-guidelines/strobe/

http://www.equator-network.org/reporting-guidelines/tripod-statement/


an ambulation as an event where a badge was detected by at least 6 ceiling sensors in the corridorbefore returning to the patient’s room. Each ambulation contains a set of segments, defined bydistance and speed, that start and end at the patient’s room. A patient’s ambulation recordcomprised the information from all ambulations, from the day of transfer to the PCU to the day ofdischarge. Digital ambulation profiles for each patient included 19 metrics (eAppendix 2 in theSupplement) associated with the total number of ambulations, ambulation frequency, and thedistance, duration, and speed associated with each ambulation (see eTable 1 and eTable 2 in theSupplement for examples). To account for the fact that transfer and discharge days often reducedthe opportunity for ambulation, parameters were calculated on the basis of the total number of daysand the number of full days (total number of days excluding the transfer and discharge days) inthe PCU.

OutcomesDischarge location (acute or subacute rehabilitation or home) and length of stay, defined as the totalnumber of days in the PCU, were extracted from the electronic health record. No patients wereinstitutionalized at baseline. Patients who were discharged to a hotel or a family member’s homewere considered to be discharged home for analytical purposes. Information on 30-day readmissionwas extracted from the electronic health record, which included information on readmissions outsideof Johns Hopkins. Readmission was defined as any overnight stay past midnight in a hospital within30 days of discharge from the initial procedure. Because adjudication of observation status orreadmission is not readily apparent in the electronic health record, patients were considered to bereadmitted if they were in a hospital (including the emergency department) past midnight. Thisapproach was chosen to identify clinically relevant cases of patients needing hospital-level medicalcare, without administrative or technical exclusions that can lead to underreporting. Administrativedata on readmissions were obtained to supplement the research classification.

Table. Patient and Surgical Characteristics

CharacteristicCardiac Surgery Patients,No. (%) (N = 100)

Age, median (IQR), y 65 (58-72)

Male 81 (81)

Race

White 82 (82)

African American 11 (11)

Other 7 (7)

Comorbidities

Prior stroke 6 (6)

Hypertension 84 (84)

Congestive heart failure 25 (25)

Peripheral vascular disease 12 (12)

Chronic obstructive pulmonary disease 12 (12)

Tobacco use (current) 17 (17)

Diabetes 43 (43)

European System for Cardiac Operative RiskEvaluation log score, median (IQR)

2.49 (1.9-4.0)

Surgery

Coronary artery bypass only 64 (64)

Valve only 17 (17)

Other 19 (19)

Cardiopulmonary bypass duration,median (IQR), min

99 (75-125)

Abbreviation: IQR, interquartile range.






Statistical AnalysisHere we report data for all 100 patients who had their IR badge at discharge from the PCU and whorecorded at least 1 voluntary ambulation (eAppendix 1 in the Supplement). To compare pairwisedifferences in outcomes between groups (yes or no readmission; yes or no discharge to acute orsubacute rehabilitation or home) for each ambulation parameter and length of stay, we used 1-wayanalysis of variance to determine F scores and 2-sided P values. P < .05 was considered statisticallysignificant. Predictions of outcomes (readmission, discharge disposition, and length of stay) wereperformed using linear regression with split sampling in SPSS statistical software version 26 (IBM).19

All models were created using the 19 ambulation parameters for 100 patients. No other patient-specific demographic or clinical variables were used. Because the number of adverse events wassmall, we first used a synthetic minority oversampling technique (imblearn.over_sampling.SMOTEalgorithm in Python version 3.7.0, Python) to balance the data before analysis.20 The SMOTEalgorithm is widely used and has been validated in various domains. After balancing, the data weresplit (70/30) into independent training and test sets, using the random number generator andcompute variable functions. Predictions of 30-day readmission and discharge disposition(dependent variables) were performed using the binary logistic regression model with the 19ambulation parameters and length of stay for all patients as covariates. Prediction of length of stayused the linear regression model, with 19 ambulation parameters as covariates. Receiver operatingcharacteristic curves were created on the basis of the predicted outcomes (readmission anddischarge location) from the test set and the corresponding outcomes from the balanced data set

Figure 1. Remote Monitoring of Patient Ambulation

Illustration of location systemA

S01-S02 S02-S03 S03-S04

Floor

CeilingS01 S02 S03 S04

Detection zone

Admitted(08:51) Discharged

(12:15)

Badge on(10:00)

Badge offOR to

ICU

ICU to PCU

PCU: 1 2 3 4

Timeline, d

Timeline for patient 84B

Badge on or offLocation changeAmbulation

1000

800

600

400

200

0

Cum

ulat

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ista

nce,

ft

Time, min

Cumulative distance and speed forthird of 3 ambulations on day 3

C

6543210

800

600

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0

Dist

ance

, ft

Ambulation No.

1 2 3 4 5 6 7 8 9

Distance for all voluntary ambulationsD

Day 1 Day 4Day 2 Day 3

1.0

0.5

0

Spee

d, m

/s

Time, min6420

A, Schematic illustration of the infrared real-timelocation system. An infrared badge, worn by thepatient, emits an infrared signal that is detected byceiling sensors in the corridors and patient rooms.Detection of a badge signal by a ceiling sensor resultsin transmission of the badge identification, the sensorlocation, and a time stamp to a server. B, Timeline forpatient 84. This patient was in the progressive careunit (PCU) for 4 days and performed 9 voluntaryambulations before discharge. C, Cumulative distancefor the third of 3 ambulations on day 3. The insetshows the speed for each segment. D, Distance for allvoluntary ambulations. To convert feet to meters,multiply by 0.3. ICU indicates intensive care unit; andOR, operating room.





(receiving operating characteristic analysis module). Data analysis was performed from June 2018 toDecember 2019.

Results

From August 29, 2016, to April 4, 2018, 238 patients consented to wear an IR badge to monitorvoluntary out-of-room ambulations while in the PCU. Analysis was performed for 100 patients (19women [19%] and 81 men [81%]) (Table; eAppendix 1 in the Supplement). Overall, the mean (SD) ageof patients in the study was 63.1 (11.6) years, and the mean (SD) length of stay in the PCU was 5.9(2.2) days. The 30-day readmission rate was 21% (21 of 100 patients), and 11 (11%) of the patientswere discharged to acute or subacute rehabilitation.

Ambulation records were obtained for 100 patients during their stay in the PCU. Each voluntaryout-of-room ambulation consists of a series of segments identified from detection of the patient’sbadge by ceiling sensors (Figure 1A). The segments trace the route for each ambulation, from whichthe distance and speed can be calculated. Overall, we recorded more than 14 000 segments in 840voluntary ambulations corresponding to a total of 127.8 km (79.4 miles). Figure 1B shows arepresentative timeline for a patient during hospitalization. This patient was in the PCU for 4 daysand during this time completed 9 voluntary ambulations. The patient was discharged to home andwas not readmitted within 30 days. For the example ambulation shown in Figure 1C, the ambulationdistance increased linearly with time, indicating that the patient maintained a constant speed. In thisambulation, the patient walked approximately 229 m (750 ft) at a mean speed of approximately 0.7m/s (Figure 1C). Ambulations can be summarized in terms of daily values, and for this patient, thetotal daily ambulation distance increased from approximately 244 m (800 ft) on the day of transferto the PCU (day 1) to approximately 610 m (2000 ft) on day 3 (Figure 1D). In this study, very fewpatients exceeded a speed of 0.6 m/s in a single ambulation.

Ambulation profiles were created for each patient and included 19 metrics associated with eachvoluntary ambulation (see eAppendix 2 in the Supplement). Examples of ambulation record andprofile for a patient who was not readmitted are provided in eFigure 1 and eTable 1 in the Supplement,and those for a patient who was readmitted within 30 days are shown in eFigure 2 and eTable 2 inthe Supplement. Summaries of ambulation profile data for all patients are shown in eTable 3 andeTable 4 in the Supplement. The ambulation profiles provide high–information content datadescribing a patient’s ambulation status and trajectory.

While they are in the PCU, patients are encouraged to complete 3 voluntary ambulations perday. From the ambulation profiles, we found that the compliance rate with 3 ambulations per day was28.0% on day 2 (the first full day after transfer to the PCU), 27.0% on day 3, and 28.0% on day 4(Figure 2). The ability to remotely track simple metrics, such as compliance with voluntaryambulations, highlights a major advantage of remote monitoring systems.

To determine the association with clinically relevant outcomes, ambulation parameters werecompared with 30-day readmission and discharge location (Figure 3; eFigure 3 and eTable 5 in theSupplement). Several ambulation parameters were statistically significant between the 2 groups(30-day readmission yes or no; discharge location yes or no). For 30-day readmissions, the 95%confidence limits for 4 ambulation parameters showed no overlap between the group notreadmitted and the group readmitted: (1) mean number of ambulations per day (ambulationfrequency) (1.60 [95% CI, 1.40-1.79] vs 1.04 [95% CI, 0.71-1.37]; F = 7.34; P = .008), (2) percentageof days with at least 1 ambulation (71.7% [95% CI, 67.1%-76.4%] vs 46.8% [95% CI, 35.9%-57.6%];F = 22.37; P < .001), (3) percentage of days with at least 3 ambulations (25.7% [95% CI, 20.3%-31.1%]vs 12.1% [95% CI, 4.4%-19.7%]; F = 5.92; P = .03), and (4) total cumulative distance for allambulations (4643.6 [95% CI, 3550.5-5736.7] vs 2501.1 [95% CI, 1527.4-3474.7] ft; F = 3.83; P = .05).Several other parameters were statistically significant, including total number of ambulations,number of days with ambulations, percentage of days with at least 2 ambulations, and change inmean ambulation speed (eTable 5 in the Supplement).











For discharge location, the 95% confidence limits for 1 parameter showed no overlap: the lengthof stay in the PCU (5.60 [5.20-5.99] vs 8.36 [6.36-10.36] days; F = 18.2; P < .001) (eFigure 3 in theSupplement). Other parameters that were statistically significant include percentage of days with atleast 1 ambulation, percentage of days with at least 2 ambulations, and maximum speed in a singleambulation (eTable 5 in the Supplement).

To gain insight into a patient’s recovery trajectory, we calculated how mean distance, speed, andtime changed between successive ambulations (eFigure 4 and eFigure 5 in the Supplement). Thetrajectory is defined in terms of the derivative of distance, speed, and time with respect toambulation number. For individuals who were discharged home and were not readmitted, theincrease in mean distance in successive ambulations was positive (eTable 3 and eTable 4 in theSupplement). However, the mean change in ambulation time was approximately 0, illustrating thatthere was little change in ambulation duration, but because their distance increased, there was astatistically significant increase in their ambulation speed compared with individuals who werereadmitted (P = .02; F = 5.3). Therefore, the rate of change in ambulation speed was an importantmetric in recovery. For individuals who were readmitted or discharged to acute or subacuterehabilitation, the derivative in distance was generally small and positive, indicating that few patientsshowed a decrease in mobility during their stay in the unit.

Having established that several ambulation parameters were statistically significant between30-day readmission (yes or no) and discharge location (acute or subacute rehabilitation or home)groups, a binary linear regression model with split sampling was used to assess outcome predictionson the basis of the ambulation profiles (eTable 6 in the Supplement). Thirty-day readmissions werepredicted with 86.7% sensitivity and 88.2% specificity, and the discharge location was predictedwith 84.6% sensitivity and 86.4% specificity. The model predictions were similar according to fulldays in the PCU (eTable 6 in the Supplement). Receiver operating characteristic curves for theregression models (Figure 4) had area under the curve (C statistic) values of 0.925 (95% CI, 0.836-1.000) for 30-day readmissions and 0.930 (95% CI, 0.855-1.000) for discharge location. Next weassessed predictions of length of stay with ambulation profiles. The correlation coefficient forpredicted of length of stay was 0.927 (see eTable 7 in the Supplement for details). In general,outcomes were improved in patients with at least 1 voluntary ambulation per day, a cumulative totalambulation distance of more than 1082 meters (3550 ft; ie, >7 laps around the PCU), a change inambulation speed of more than 0.018 m/s per ambulation, and a length of stay of less than 6 days.

Figure 2. Compliance With Number of Completed Voluntary Ambulations per Day

100

80

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40

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plia

nce,

%

Day1 2 3 4 5 6

3 Voluntary ambulations per dayA

100

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%

Day1 2 3 4 5 6

2 Voluntary ambulations per dayB

100

80

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0

Com

plia

nce,

%

Day1 2 3 4 5 6

1 Voluntary ambulation per dayC

A-C, Graphs show data for 3 voluntary ambulations per day (A), 2 voluntary ambulationsper day (B), and 1 voluntary ambulation per day (C). The mean (SD) length of stay was5.9 (2.2) days. The total numbers of individuals were 100 at days 1, 2, and 3; 92 at day 4;

73 at day 5; and 47 at day 6. Data are shown for up to day 6 in the cardiac surgeryprogressive care unit.











Figure 3. Comparison of Ambulation Parameters for 30-Day Readmission

0 5 1510

3.84Total ambulations, No.8.95 (7.70-10.19)6.33 (4.09-8.58)

0 1 32

7.34Ambulation frequency, No./LOS1.60 (1.40-1.79)1.04 (0.71-1.37)

0 5 10

8.02Days with ambulations, No.4.03 (3.66-4.39)2.86 (2.01-3.70)

FScore

Ambulation parameters

Mean(95% CI)

0 5 1510

0.12LOS on PCU, d5.86 (5.34-6.38)6.05 (5.27-6.83)

0 1000500Mean (95% CI)

0.69Shortest ambulation257.8 (230.9-284.7)234.0 (187.8-280.1)

Ambulation distance, ft

0 500 1000

2.67Mean ambulation distance (all)463.7 (404.1-523.3)365.4 (304.7-426.2)


0 500 15001000

1.12Longest ambulation905.2 (564.7-1245.7)549.4 (350.2-748.7)


0 2000 60004000

3.83Total distance4643.6 (3550.5-5736.7)2501.1 (1527.4-3474.7)


0 10050

5.92Days with 3 per day25.7 (20.30-31.10)12.1 (4.40-19.70)

Compliance, %

0 10050

5.34Days with 2 per day46.4 (40.50-52.20)32.1 (22.60-41.70)

Compliance, %

0 50 100

22.37Days with 1 per day71.7 (67.10-76.40)46.8 (35.90-57.60)

Compliance, %

–0.05 0 0.05Mean (95% CI)

5.33Change in ambulation mean speed0.028 (0.018-0.039)0.003 (–0.013-0.019)

Derivatives

c1 0 1

0.30Change in ambulation duration0.03 (0.02-0.04)0.00 (–0.01-0.02)

Derivatives

–20 40200 60

1.23Change in ambulation distance38.80 (25.10-52.50)22.30 (–4.00-48.60)

Derivatives

0 0.2 0.60.4

1.01Minimum mean speed in a singleambulation

0.22 (0.19-0.25)0.25 (0.19-0.32)

Ambulation speed, m/s

0 0.2 0.60.4

0.18Mean speed (all ambulations)0.38 (0.35-0.41)0.37 (0.29-0.44)


0 0.2 0.60.4

1.84Max mean speed in a singleambulation

0.54 (0.50-0.59)0.47 (0.36-0.58)


0 5 1510

0.61Shortest ambulation4.00 (3.60-4.30)3.60 (2.70-4.50)

Ambulation speed, min

0 5 1510

1.99Mean ambulation distance (all)6.70 (6.20-7.20)5.80 (4.40-7.20)


FScore

Ambulation parameters

Mean(95% CI)

0 5 1510

1.68Longest ambulation12.40 (10.10-14.60)9.40 (6.40-12.30)


Not readmitted Readmitted

Comparisons are based on total days in the cardiac surgery progressive care unit (PCU) for 100 patients. To convert feet to meters, multiply by 0.3. LOS indicates length of stay.




Discussion

The findings of this study suggest that a real-time location system, already installed in the hospital fortracking the locations of staff and equipment, can be used to monitor voluntary out-of-roomambulations in a cardiac surgery PCU. From the ambulation data, we defined a patient’s digitalambulation profile, which included 19 ambulation parameters (eg, number, frequency, distance, andspeed) and enabled predictions of outcomes.

Patient mobility is an important biomarker associated with postoperative recovery; however,accurate measurement remains challenging. A systematic review of risk prediction models forhospital readmissions, based solely on clinical parameters, concluded that they generally performpoorly,5 highlighting the need for measurement tools that have high information content.

Remote tracking and wearable accelerometers both provide measures of mobility but havesome key differences: remote tracking can distinguish out-of-room ambulation events and canprovide information on patient speed, although the resolution (approximately 6 m [20 ft] in thisstudy) is larger than a single step. Furthermore, remote tracking is easy to implement and is scalable.

To promote mobility, patients in the PCU are encouraged to walk 3 times per day as part of theActivity & Mobility Promotion program.9 We found that the maximum compliance, on the first fullday after transfer to the PCU, was 28.0%, and decreased on subsequent days, largely because themore ambulatory patients were generally discharged sooner (mean [SD] length of stay, 5.9 [2.2]days). Nonetheless, these results highlight the value of remote monitoring in assessing complianceto mobility guidelines. In general, outcomes were improved in patients with at least 1 voluntaryambulation per day, a cumulative total ambulation distance of more than 1082 meters (3550 ft; ie, >7laps around the PCU), a change in ambulation speed of more than 0.018 m/s per ambulation, and alength of stay of less than 6 days.

Studies of hospitalized patients with wearable accelerometers have found statisticallysignificant differences in patient outcomes with daily step counts. One study16 found that 55% ofparticipants with fewer than 900 steps per day experienced hospitalization-acquired functionaldecline, compared with 18% of participants with 900 or more steps per day. Another study17 ofpostsurgical patients showed that the numbers of steps on the second, third, and fourth recoverydays were statistically different between patients discharged to home compared with thosedischarged to a skilled nursing facility or home with health care. A third study10 showed that athreshold of 275 steps per day enabled prediction of 30-day readmission with a sensitivity of 42%and a specificity of 78%.

Figure 4. Receiver Operating Characteristic (ROC) Curves for 30-Day Readmissions and Discharge Location, and Predictions of Length of Stay (LOS)

1.0

0.8

0.6

0.4

0.2

00 1.00.8

Sens

itivi

ty, %

1 – Specificity, %0.60.40.2

30-Day readmissionA

1.0

0.8

0.6

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Discharge locationB

12

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00 1210

Pred

icte

d LO

S, d

Actual LOS, d862 4

LOSC

A, ROC curve for 30-day readmission (C statistic, 0.925; 95% CI, 0.836-1.000). B, ROCcurve for discharge location (C statistic, 0.930; 95% CI, 0.855-1.000). The area underthe ROC curve was calculated using a nonparametric (distribution-free) method. C, ROC

curve for LOS. Models were generated using split sampling (70/30) following balancingbased on data for the total number of days on the unit. Data shown are for the test set.




Although step count is a convenient metric, it does not provide information about speed andonly provides an indirect measurement of distance. Several groups have shown that a short (4 or 6m) gait speed test is correlated with survival in elderly adults,21,22 and thresholds for definingdismobility (very low gait speeds) of 0.6 to 0.8 m/s (1.34-1.70 mph) have been proposed for thegeneral population. In the present study of a postsurgical population, very few patients exceeded 0.6m/s in a single ambulation. However, all individuals who were not readmitted and discharged homeshowed a significant increase in speed in successive ambulations.

Prior studies10,16,17,21,22 highlight the fact that step count and gait speed are associated withclinical outcomes; however, our results show that more detailed measurements of mobility withhigher information content enable a significant increase in predictive power. We incorporateelements of step count (ambulation distance and related parameters) and gait speed (ambulationspeed and related parameters), as well as parameters associated with frequency, and changes inthese parameters in successive ambulations. Together, these parameters enable improvedpredictions of clinically relevant outcomes. Comparison of our results with previous studies alsosuggest that the criteria for identification of at-risk patients is likely not generalizable and is differentfor different patient populations.

The real-time location system enables accurate reconstruction of the individual segments thatconstitute an ambulation. Our algorithms take into account anomalies, such as a missing sensor in asequence that may occur if the badge is obstructed during that segment. Although there are sensorsin patient rooms, the technology does not enable assessment of in-room activity. Here we arbitrarilydefine an ambulation as an event that begins and ends in a patient room, without leaving the unit. Weignored events where a patient left the unit, either on a voluntary ambulation or for medical reasons.A complication associated with leaving the unit for a test is that we cannot, at present, distinguishbetween ambulation and transport by wheelchair. Because data can be collected and analyzed inreal-time, a patient’s ambulation history could be updated and displayed in the electronic medicalrecord or on a patient’s smartphone in real time. The ability to track and monitor ambulation historycould enable goal setting and identification of at-risk patients based on real-time prediction models.

LimitationsThis study has some limitations. It is a prognostic cohort study that was performed in a cardiacsurgery PCU with a small sample size. Enrollment criteria were broad; hence, our results can beconsidered broadly generalizable. Data were analyzed for all patients who had their IR badge atdischarge from the PCU and recorded at least 1 voluntary ambulation. Exclusion due to a lost orremoved badge or failure to register at least 1 voluntary ambulation may have resulted in a selectionbias. To improve patient compliance, alternative methods for wearing the IR badge should beexplored. Exclusion of out-of-unit ambulations may have led to underestimating the total number ofambulations. Further studies will also be needed to assess the technology in other patientpopulations. Outcomes predictions were based solely on metrics from voluntary out-of-roomambulations; however, measurement of in-room activity and input from other patient-specificdemographic or clinical variables could provide additional insight into patient functional status andoutcomes.

Conclusions

Real-time location system technology was used to monitor voluntary ambulations of patients in acardiac surgery PCU. Each ambulation contains information on distance and speed, whereas apatient’s ambulation history provides insight into ambulation frequency and changes in ambulationmetrics. Metrics associated with voluntary ambulations were predictive of outcomes such asreadmission and discharge disposition.




ARTICLE INFORMATIONAccepted for Publication: December 10, 2020.

Published: March 17, 2020. doi:10.1001/jamanetworkopen.2020.1074

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Jeong IC et al.JAMA Network Open.

Corresponding Author: Peter C. Searson, PhD, Department of Materials Science and Engineering, Johns HopkinsUniversity, 120 Croft Hall, 3400 N Charles St, Baltimore, MD 21218 ([email protected]).

Author Affiliations: inHealth, Johns Hopkins Individualized Health Initiative, Johns Hopkins University School ofMedicine, Baltimore, Maryland (Jeong, Zahradka, Searson); Department of Critical Care and Anesthesiology, JohnsHopkins University School of Medicine, Baltimore, Maryland (Healy, Madeira, Brown); Department of BiomedicalEngineering, Johns Hopkins University, Baltimore, Maryland (Bao, Searson); Department of Computer Science,Johns Hopkins University School of Medicine, Baltimore, Maryland (Xie); Department of Surgery, Johns HopkinsUniversity School of Medicine, Baltimore, Maryland (Sussman, Whitman); Department of Epidemiology, JohnsHopkins University Bloomberg School of Public Health, Baltimore, Maryland (Schrack); Department of PhysicalMedicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, Maryland (Hoyer, Searson);Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, Maryland (Searson).

Author Contributions: Dr Searson had full access to all of the data in the study and takes responsibility for theintegrity of the data and the accuracy of the data analysis. Drs Brown and Searson are co–senior authors.

Concept and design: Jeong, Madeira, Sussman, Whitman, Schrack, Hoyer, Brown, Searson.

Acquisition, analysis, or interpretation of data: Jeong, Healy, Bao, Xie, Zahradka, Searson.

Drafting of the manuscript: Jeong, Searson.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Jeong, Bao.

Obtained funding: Brown, Searson.

Administrative, technical, or material support: Jeong, Bao, Madeira, Schrack, Searson.

Supervision: Whitman, Schrack, Brown, Searson.

Conflict of Interest Disclosures: Dr Schrack reported receiving grants from the National Institute of Aging outsidethe submitted work. Dr Brown reported receiving grants from Johns Hopkins inHealth, support from Medtronic,and a grant from the National Institutes of Health outside the submitted work. Dr Searson reported receivinggrants from the National Institutes for Neurological Disorders and Stroke, the Cystic Fibrosis Foundation, and theDefense Threat Reduction Agency outside the submitted work. No other disclosures were reported.

Funding/Support: This work was support in part by inHealth, the Johns Hopkins precision medicine initiative.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection,management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; anddecision to submit the manuscript for publication.

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SUPPLEMENT.eAppendix 1. Study Criteria and Patient Flow CharteAppendix 2. Parameters in a Patient’s Ambulation ProfileeFigure 1. Timeline and Ambulation History for Patient 83eTable 1. Ambulation Profile for Patient 83eFigure 2. Timeline and Ambulation History for Patient 92eTable 2. Ambulation Profile for Patient 92eTable 3. Summary of Ambulation Parameters for 100 Patients (Total Days on the PCU)eTable 4. Summary of Ambulation Data for 100 Patients (Full Days on the PCU)eFigure 3. Comparison of Ambulation Parameters for 30-Day Readmission and Discharge to Acute/SubacuteRehabeTable 5. Summary of F and P Values for Comparison of Outcomes Between Groups (Readmission Yes/No;Discharge Location Home/Acute Rehab) for Each of the 19 Ambulation Parameters and Length of StayeFigure 4. Examples of Ambulation Distance (dn), Time (tn), and Speed (sn) Versus Ambulation Number (n)eFigure 5. Comparison of Ambulation Distance (dn), Time (tn), and Speed (sn) Versus Ambulation Number (n) andTime on the PCU (Beginning at 00:00 on the Transfer Day)eTable 6. Comparison of Prediction Models for 30-Day Readmission and Discharge Location Based on Total Daysand Full Days on the PCUeTable 7. Prediction of Length of Stay (LoS)




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