Predicting outcomes in patients undergoing pancreatectomy using wearable technology and machine learning: Prospective cohort study

BACKGROUND: Pancreatic cancer is the third leading cause of cancer-related deaths, and although pancreatectomy is currently the only curative treatment, it is associated with significant morbidity. OBJECTIVE: The objective of this study was to evaluate the utility of wearable telemonitoring technologies to predict treatment outcomes using patient activity metrics and machine learning. METHODS: In this prospective, single-center, single-cohort study, patients scheduled for pancreatectomy were provided with a wearable telemonitoring device to be worn prior to surgery. Patient clinical data were collected and all patients were evaluated using the American College of Surgeons National Surgical Quality Improvement Program surgical risk calculator (ACS-NSQIP SRC). Machine learning models were developed to predict whether patients would have a textbook outcome and compared with the ACS-NSQIP SRC using area under the receiver operating characteristic (AUROC) curves. RESULTS: Between February 2019 and February 2020, 48 patients completed the study. Patient activity metrics were collected over an average of 27.8 days before surgery. Patients took an average of 4162.1 (SD 4052.6) steps per day and had an average heart rate of 75.6 (SD 14.8) beats per minute. Twenty-eight (58%) patients had a textbook outcome after pancreatectomy. The group of 20 (42%) patients who did not have a textbook outcome included 14 patients with severe complications and 11 patients requiring readmission. The ACS-NSQIP SRC had an AUROC curve of 0.6333 to predict failure to achieve a textbook outcome, while our model combining patient clinical characteristics and patient activity data achieved the highest performance with an AUROC curve of 0.7875. CONCLUSIONS: Machine learning models outperformed ACS-NSQIP SRC estimates in predicting textbook outcomes after pancreatectomy. The highest performance was observed when machine learning models incorporated patient clinical characteristics and activity metrics

Puettavan teknologian terveysvaikutukset

Tiivistelmä. Tutkielmassa tarkasteltiin puettavien teknologioiden terveyshyötyjä. Tarkemmin sitä, vaikuttavatko ne käyttäjien aktiivisuuteen tai lisäävätkö ne käyttäjien liikuntaa, sekä onko tällä mitään terveydellisiä hyötyjä. Tutkielmassa tarkasteltiin myös niitä tapoja, joilla puettavat teknologiat pyrkivät vaikuttamaan käyttäjien aktiivisuuteen. Puettavien teknologioiden käyttö on yleistynyt paljon, sekä niin on ylipainokin. Tutkielmassa pohdittiin, voitaisiinko toisesta näistä kahdesta yleistyvästä trendistä saada helpotuksia toisen trendin hallitsemiseen. Voisiko puettavilla älylaitteilla olla potentiaalia kansanterveyden edistäjänä? Tutkielma on toteutettu aikaisemman tutkimuksen pohjalta kirjallisuuskatsauksena. Tutkielman tuloksena huomattiin, että puettavat teknologiat lisäävät käyttäjien fyysistä aktiivisuutta. Puettavat teknologiat kuuluvat yleensä suostuttelevien teknologioiden piiriin, jotka nimensäkin mukaan houkuttelevat käyttäjiä muuttamaan käytöstään jollain tavalla. Suoria terveyshyötyjä ei puettavilla teknologioilla käsitellyissä tutkimuksissa ainakaan merkittävässä määrin löytynyt. Tutkimuksissa havaittiin kumminkin puettavien teknologioiden lisäävän käyttäjien fyysisetä aktiivisuutta ja fyysisen aktiivisuuden lisäämisellä on tutkittuja terveyshyötyjä. Kyseinen ristiriita voi johtua muun muassa siitä, että käsitellyt tutkimukset olivat yleensä suhteellisen lyhyitä huomatakseen suuria muutoksia käyttäjien terveydessä. Fyysisen aktiivisuuden lisääntymisellä on tutkittu olevan monia terveyshyötyjä. Sen vuoksi on luotukin sekä päivittäiset, että viikoittaiset suositukset fyysiselle aktiivisuudelle, joita noudattamalla saa suuren osan fyysisen aktiivisuuden tuomista terveyshyödyistä. Fyysisen aktiivisuuden on todettu madaltavan riskiä saada monia sairauksia. Fyysisen aktiivisuuden lisääntymisellä on myös huomattu mielenterveydellisiä hyötyjä. Henkilöiden, jotka ovat harrastaneet enemmän fyysisiä aktiviteettejä on huomattu saavan parempia tuloksi mielenterveyttä mittaavissa testeissä. Puettavia teknologioita voitaisiin käyttää yhdessä jonkin muun ohjeistuksen tai muun vastaavan kanssa tulevaisuudessa jopa kansanterveyden edistämisen näkökulmasta. Tulevaisuudessa voitaisiinkin siis tehdä tiiviimpää yhteistyötä terveydenhuollon sektorin kanssa. Laitteita voitaisiin parantaa ja suunnitella enemmän terveydenhuollon sektorin käyttöön. Sekä yhdistää laitteiden käyttö terveydenhuollon nykyisten menetelmien kanssa, vaikka auttaakseen ruokavalio ohjeistuksen kanssa painonhallinnassa

Measuring health status using wearable devices for patients undergoing radical cystectomy

Wearable devices (WDs) are an untapped resource for measuring patient health status during the peri-operative period. The overarching aim of this thesis is to explore the potential for WDs to be used in the clinical setting for patients undergoing radical cystectomy (RC) for bladder cancer. The lack of consensus regarding the optimal approach for RC presents an opportunity to design an RCT comparing open (ORC) and robotic (RARC) RC, in which a wearable device sub-study can be embedded. While the intracorporeal Robotic vs Open Cystectomy (iROC) trial will address the comparison between ORC and RARC, my thesis focuses on exploring the clinical utility of WDs. I present the results of a systematic review of RCTs comparing ORC and RARC. Meta-analysis shows no significant difference in peri-operative and oncological outcomes between ORC and RARC. Additionally, I systematically review healthcare studies using WDs and highlight the findings, device choices and device metrics used. Step-count is the most frequently collected WD metric, and chronic health conditions are the focus of majority of studies. Findings from these systematic reviews guided the design of the iROC trial protocol. I present the pre-planned interim analysis of the iROC trial, and explore associations between WD data and pre-operative health measures including cardiopulmonary exercise testing (CPET). Step-count correlates with the CPET variables (p < 0.01) routinely used to risk-stratify patients undergoing RC, and is the only predictor of major complications following RC in a logistic regression model. Finally, I evaluate recovery of baseline step-count at three months post-operatively as a predictor of overall survival. Applying a threshold of 50% recovery at 3 months, step-count predicts one-year survival to a sensitivity and specificity of 100% and 93% respectively. My findings highlight the potential of WDs in peri-operative care, and my post-doctoral work will progress this work further

Remote timed up and go evaluation from activities of daily living reveals changing mobility after surgery

Background: Mobility impairment is common in older adults and negatively influences the quality of life. Mobility level may change rapidly following surgery or hospitalization in the elderly. The timed up and go (TUG) is a simple, frequently used clinical test for functional mobility; however, TUG requires supervision from a trained clinician, resulting in infrequent assessments. Additionally, assessment by TUG in clinic settings may not be completely representative of the individual's mobility in their home environment. Objective: In this paper, we introduce a method to estimate TUG from activities detected in free-living, enabling continuous remote mobility monitoring without expert supervision. The method is used to monitor changes in mobility following total hip arthroplasty (THA). Methods: Community-living elderly (n = 239, 65-91 years) performed a standardized TUG in a laboratory and wore a wearable pendant device that recorded accelerometer and barometric sensor data for at least three days. Activities of daily living (ADLs), including walks and sit-to-stand transitions, and their related mobility features were extracted and used to develop a regularized linear model for remote TUG test estimation. Changes in the remote TUG were evaluated in orthopaedic patients (n = 15, 55-75 years), during 12-weeks period following THA. Main results: In leave-one-out-cross-validation (LOOCV), a strong correlation (p = 0.70) was observed between the new remote TUG and standardized TUG times. Test-retest reliability of 3-days estimates was high (ICC = 0.94). Compared to week 2 post-THA, remote TUG was significantly improved at week 6 (11.7 +/- 3.9 s versus 8.0 +/- 1.8 s,p &lt;0.001), with no further change at 12-weeks (8.1 +/- 3.9s, p = 0.37). Significance: Remote TUG can be estimated in older adults using 3-days of ADLs data recorded using a wearable pendant. Remote TUG has discriminatory potential for identifying frail elderly and may provide a convenient way to monitor changes in mobility in unsupervised settings.</p

Assessment and optimisation of wearable activity monitors within an enhanced recovery framework

Enhanced recovery after surgery (ERAS) is a model of care that aims to improve patient recovery after surgery. Wearable activity monitors (WAMs) have the potential to provide possible solutions to a wide range of clinical challenges. The aim of this thesis was to assess whether physical activity, measured by a WAM, can be used as a measurable marker of peri-operative well-being and recovery after surgery, and whether the WAM can therefore be used to assess and help improve recovery after surgery. A wrist-worn WAM was utilised to measure physical activity in a healthy normal cohort showing that it was feasible to monitor continuous physical activity in a healthy cohort in a free-living environment. Activity data were processed both at an individual level and as a group allowing further analysis and comparator with the surgical patient cohort. The WAM was used to measure objective physical activity data for a cohort of patients undergoing colorectal surgery. Activity was assessed pre-operatively at home, post-operatively on the in-patient ward and then on discharge home back into the community. The physical activity data gave insight into patients’ baseline function and their progression and recovery following their surgical procedure, with more detailed analysis showing the WAM’s ability to reflect the daily activities on the ward. There were statistically significant correlations between peri-operative physical activity and post-operative outcomes. The results from the use of WAMs within this thesis provide an opportunity for refining the ERAS concept through continuous, objective physical activity monitoring as well as the potential to enhance patient/clinician communication, leading to more personalised care and an improvement in post-operative outcomes.Open Acces