CClinicalTrials.gg
Status unknownNCT05534178PLANTOLOGYUpdated Sep 9, 2022

Machine Learning Model to Predict HOLS and Mortality After Discharge in Hospitalized Oncologic Patients

An observational study in Solid Tumor, Nutrition Related Neoplasm/Cancer and Comorbidities and Coexisting Conditions, sponsored by Vall d'Hebron Institute of Oncology. Status unknown at 3 sites in Spain. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2022-09-09.

Sponsored by Vall d'Hebron Institute of Oncology · Observational

The sponsor has not verified this record recently (last verified Sep 2022), so the status shown — last known as Recruiting — may be out of date.
Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
2,500
Ages
18 Years and older
Sex
All
01

Study summary

The study aims to understand which are the most relevant parameters at admission which may allow to predict the hospital length of stay (HOLS) and mortality after discharge of oncologic hospitalized patients.

This is the first multicentric prospective observational study that tries to understand the complexity of the hospitalized oncologic patients. A comprehensive analysis will be performed with the help of the nutrition, nursery, internal medicine and oncology teams.

Read the detailed description

BACKGROUND:

Cancer is the second leading cause of death worldwide and is responsible for about 18.1 million new cases and 9.6 million deaths in 2018 alone according to the International Agency for Research on Cancer. Cancer is anticipated to rank as the leading cause of death and the most important barrier to increasing life expectancy in every country of the world in the mid-21st century1. The economic impact of cancer is significant. The annual economic cost of cancer in 2010 was estimated at approximately US$ 1.16 trillion. The reasons are complex but both cancer incidence and mortality are increasing worldwide due to aging and increasing risk factors for cancer, several of which are associated with socioeconomic development. Cancer will probably soon reach the top leading cause of death due to the rapid population growth and the declines in mortality rates by stroke or coronary heart disease in many developed countries.

Cancer patients often require inpatient care due to treatment toxicities, complications from cancer such as thrombosis, illness not related to the disease itself or terminally ill patients. Among these individuals, their treatment should balance prolongation of survival and maximization of the quality of remaining life. However, hospitalization is a stressful event for individuals with advanced cancer and their caregivers. Hospitalization often antagonizes these goals, contributing to the high cost of cancer care, worsens survival, and is increasingly recognized as poor-quality cancer care. Thus, interventions that reduce unnecessary hospitalizations, or shorten them, will likely improve quality of life and reduce costs.

Some studies relate malnutrition, which presents a marked sarcopenia and loss of lean mass, with prolonged hospitalization, reduced response to treatment, a worse overall survival and impaired quality of life. A study published in 2007 found that lung cancer patients had a longer hospitalization and required inpatient hospital treatment more frequently than any other type of tumor. Moreover, in the surgical setting there have been studies linking preoperative opioid usage and increased opioid doses with increased length of stay. Based on this data, there have been protocols developed like the ERAS (Enhanced recovery after surgery) applied first to colorectal cancer and now being tested in other settings like head and neck and gynecologic tumors, showing that it is possible to reduce opioid use with good pain control and a statistically significant shorter average length of stay.

Prognostic factors for oncologic patients after surgery or curative systemic treatment have been described, but there is no solid evidence on which combination of parameters predict mortality after hospitalization of metastatic cancer patients under active treatment. A potential solution to improve this scenario might be nutritional support to malnourished cancer patients that also has proven to be effective in shorten hospital stay and improve survival, or community based palliative care interventions that are proven to improve quality of life and reduce costs of terminally ill patients. Thus, a prognostic tool would be useful to help physicians adjust medical interventions for hospitalized cancer patients.

To the best of our knowledge, this is the first study that examines independent clinical, psychological, nutritional status, and laboratory characteristics of oncologic patients in order to grasp a comprehensive picture of what factors play a role in the length of stay, mortality, and quality of life.

MEANING The investigators pretend with this work to fill a gap of knowledge in the oncology field through a prospective study. The investigators would like to measure the effect of hospitalization on oncologic patients after discharge and how clinical and laboratory parameters at admission may be able to predict HOLS and 30-day mortality after discharge. The investigators would also like to validate the different scales already published to assess nutritional status, psychological status, quality of life or prediction of rehospitalization for oncologic patients in all-in-one study.

This study will hopefully be able to develop a predictive tool at admission to help physicians adjust medical interventions and detect possible actions that will need to be implemented during hospitalization in order to improve the overall survival and quality of life of our patients.

02

Conditions studied

  • Solid Tumor
  • Nutrition Related Neoplasm/Cancer
  • Comorbidities and Coexisting Conditions
  • Mental Status Change
  • Artificial Intelligence
  • Oncology
  • Tumor
  • Quality of Life

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Keywords

  • Hospitalization
  • Oncologic patients
03

In context

Neoplasms

9,359 studies on the registry are indexed under Neoplasms; 2,486 are open to participants now.

This study's planned enrollment of 2,500 is above the median of 205 across 1,680 observational studies indexed under Neoplasms.

Browse Neoplasms studies →

Lead sponsor

Vall d'Hebron Institute of Oncology is the lead sponsor of 21 studies on the registry; 14 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years and older
Sexes eligible
All
Sampling method
Non-probability sample

Study population

All oncologic patients that require hospitalization.

Inclusion criteria

  • ≥18 years-old.
  • Histological cancer confirmation.
  • Hospitalization in oncology ward.

Exclusion criteria

Exclusion Criteria:

  • \<18 years-old.
  • Not histological malignancy confirmed.
  • Less than 24 hours in the hospital.
05

Study design

Observational model
Cohort
Time perspective
Prospective
Enrollment
2,500 participants (estimated)
Target follow-up
4 Years
Patient registry
Yes
06

What researchers measure

Primary outcomes

  1. Predict Mortality

    Mortality at 30-day after discharge

    Time frame: 30 days after discharge

  2. Predict hospital length of stay

    Number of days hospitalized

    Time frame: Through study completion, an average of 3 years

Secondary outcomes

  1. Measure the impact of Anxiety and Depression

    Hospital Anxiety and Depression Scale (HADS). Minimum: 0 Maximum: 21. More than 12 points is clinical significant for depression or anxiety \[0-7 = Normal; 8-10 = Borderline; abnormal (borderline case)\] 11-21 = Abnormal (case)

    Time frame: Within 24 hours of admission

  2. Measure the impact of Quality of life (QoL)

    EORTC QLQ-C30 Minimum: 30 Maximum: 126. More punctuation, worse QoL

    Time frame: Within 24 hours of admission

  3. Validate standardized test HOSPITAL score: Risk of readmission

    HOSPITAL Score; higher score, more risk of readmission. Min: 0 points. Max: 13 points. Measures: Risk of potentially avoidable 30-day readmission.

    Time frame: Evaluated at discharge through study completion, an average of 3 years. The outcome is the probability of readmission within the first 30 days after discharge.

  4. Sarcopenia Assessment

    Chair test is performed by the number of stands a person can complete in 30 seconds. Results depend on sex and age, the more the better and screens for sarcopenia.

    Time frame: Within 24 hours of admission and 24 hours before discharge through study completion, an average of 3 years

  5. Sarcopenia Test

    Hand grip test; the more power the better using a hand-held dynamometer. It screens for sarcopenia. A poor performance is 20 to 22kg of power, a below average performance is 23 to 35kg of power, an average performance is 26 to 29kg of power, and an above average performance is 30 to 33kg of power.

    Time frame: Within 24 hours of admission and 24 hours before discharge through study completion, an average of 3 years

  6. Nutrition Assessment

    Intake compliance of food served (100%, 75%, 50%, 25% and 0% of the food served in each meal.

    Time frame: Within 24 hours of admission and 24 hours before discharge through study completion, an average of 3 years

  7. Opioids Intake

    Quantity of morphine equivalent in mg per day at admission and at discharge

    Time frame: Within 24 hours of admission and 24 hours before discharge through study completion, an average of 3 years

  8. Tumor Characteristics and Comorbidities

    Tumor type, oncologic treatment during the last 6 months, comorbidities measured through the Charlson score (Age, \<50years 0 points, 50-59years 1 point, 60-69 years 2 points, 70-79 years 3 points, =\>80 years, 4 points, Prior myocardial infarction 1 point; congestive heart failure 1 point; peripheral vascular disease 1point; Cerebrovascular disease 1 point; Dementia 1 point; Chronic pulmonary disease 1 point; Rheumatologic disease 1 point; Peptic ulcer disease 1 point; Mild liver disease 1 point; Diabetes 1 point; Cerebrovascular (hemiplegia)event 2 points; Moderate-to-severe renal disease 2 points; Cancer without metastases 2 points; Leukemia 2 points; Lymphoma 2 points; Moderate to severe liver disease 3 points; Metastatic Solid tumor 6 points; Acquired immune deficiency syndrome (AIDS; 6 points). Max 37 points. Min 0 points. The higher the score, the more comorbidities and worse survival.

    Time frame: Within 24 hours of admission through study completion, an average of 3 years

07

Study locations

3 of 3 sites recruiting
  • Hospital del Mar
    Barcelona, 08003, Spain
    Recruiting
  • Hospital Universitari Vall d'Hebron
    Barcelona, 08035, Spain
    • Oriol Mirallas, MD · Contact · omirallas@vhebron.net · 934 89 30 00
    • Clara Salva, MD · Sub investigator
    • Oriol Mirallas, MD · Principal investigator
    • Daniel López-Valbuena, MD · Sub investigator
    • Diego Gómez-Puerto, MD · Sub investigator
    • Kreina Sharela Vega, MD · Sub investigator
    • Jose Maria Ucha, MD · Sub investigator
    • Sergio Bueno, MD · Sub investigator
    Recruiting
  • Hospital de la Santa Creu i Sant Pau
    Barcelona, 08041, Spain
    • Berta Martin Cullell, MD · Contact · bmartinc@santpau.cat · +34935565638
    • David Paez, MD · Contact · dpaez@santpau.cat
    • Berta Martin-Cullell · Principal investigator
    • Judit Sanz · Sub investigator
    Recruiting
08

References and documents

Publications

  • Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249. doi: 10.3322/caac.21660. Epub 2021 Feb 4. PubMed 33538338 ↗
  • Brooks GA, Cronin AM, Uno H, Schrag D, Keating NL, Mack JW. Intensity of Medical Interventions between Diagnosis and Death in Patients with Advanced Lung and Colorectal Cancer: A CanCORS Analysis. J Palliat Med. 2016 Jan;19(1):42-50. doi: 10.1089/jpm.2015.0190. Epub 2015 Nov 24. PubMed 26600474 ↗
  • Manzano JG, Luo R, Elting LS, George M, Suarez-Almazor ME. Patterns and predictors of unplanned hospitalization in a population-based cohort of elderly patients with GI cancer. J Clin Oncol. 2014 Nov 1;32(31):3527-33. doi: 10.1200/JCO.2014.55.3131. Epub 2014 Oct 6. PubMed 25287830 ↗
  • Earle CC, Park ER, Lai B, Weeks JC, Ayanian JZ, Block S. Identifying potential indicators of the quality of end-of-life cancer care from administrative data. J Clin Oncol. 2003 Mar 15;21(6):1133-8. doi: 10.1200/JCO.2003.03.059. PubMed 12637481 ↗
  • Whitney RL, Bell JF, Tancredi DJ, Romano PS, Bold RJ, Joseph JG. Hospitalization Rates and Predictors of Rehospitalization Among Individuals With Advanced Cancer in the Year After Diagnosis. J Clin Oncol. 2017 Nov 1;35(31):3610-3617. doi: 10.1200/JCO.2017.72.4963. Epub 2017 Aug 29. PubMed 28850290 ↗

Study documents

  • Protocol and statistical analysis plan · Aug 13, 2020
  • Informed consent form · Aug 13, 2020

Documents are hosted by the registry — open the source record to download them.

Individual participant data

Plan to share: Yes — IPD will be shared with all the participants of the study

Supporting information: Study protocol, Icf, Csr

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Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Sep 9, 2022, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
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Registry details

Key details

Study ID
NCT05534178
Lead sponsor
Vall d'Hebron Institute of Oncology
Collaborators
Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau, Hospital del Mar
Responsible party
Oriol Mirallas (Principal Investigator, Vall d'Hebron Institute of Oncology) — Principal investigator
First posted
Sep 9, 2022
Start date
Feb 15, 2020
Primary completion
Feb 15, 2023 (estimated)
Completion
Mar 15, 2024 (estimated)
Last update
Sep 9, 2022

Study contacts

Oriol Mirallas, MD
Contact
omirallas@vhebron.net
+34934 89 30 00
Oriol Mirallas, MD
principal investigator · Vall d'Hebron University Hospital

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

Not currently enrolling

This study is status unknown, as verified in Sep 2022. You cannot join it, but the record below documents what was studied.

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