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

September 5, 2022 updated by: Oriol Mirallas, Vall d'Hebron Institute of Oncology

Machine Learning Model to Predict Hospital Length of Stay (HOLS) and Mortality After Discharge in Hospitalized Oncologic Patients [Plantology Database]: a Multicenter Cross-validation Study

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.

Study Overview

Status

Recruiting

Conditions

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.

Study Type

Observational

Enrollment (Anticipated)

2500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Spain
      • Barcelona, Spain, 08035
        • Recruiting
        • Hospital Universitari Vall d'Hebron
        • Contact:
        • Sub-Investigator:
          • Clara Salva, MD
        • Principal Investigator:
          • Oriol Mirallas, MD
        • Sub-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
      • Barcelona, Spain, 08003
        • Recruiting
        • Hospital del Mar
        • Contact:
        • Sub-Investigator:
          • Jordi Recuero, MD
        • Principal Investigator:
          • Sonia Servitja, MD
      • Barcelona, Spain, 08041
        • Recruiting
        • Hospital de la Santa Creu i Sant Pau
        • Contact:
        • Contact:
        • Principal Investigator:
          • Berta Martin-Cullell
        • Sub-Investigator:
          • Judit Sanz

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

N/A

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

All oncologic patients that require hospitalization.

Description

Inclusion Criteria:

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

Exclusion Criteria:

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Predict Mortality
Time Frame: 30 days after discharge
Mortality at 30-day after discharge
30 days after discharge
Predict hospital length of stay
Time Frame: Through study completion, an average of 3 years
Number of days hospitalized
Through study completion, an average of 3 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Measure the impact of Anxiety and Depression
Time Frame: Within 24 hours of admission
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)
Within 24 hours of admission
Measure the impact of Quality of life (QoL)
Time Frame: Within 24 hours of admission
EORTC QLQ-C30 Minimum: 30 Maximum: 126. More punctuation, worse QoL
Within 24 hours of admission
Validate standardized test HOSPITAL score: Risk of 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.
HOSPITAL Score; higher score, more risk of readmission. Min: 0 points. Max: 13 points. Measures: Risk of potentially avoidable 30-day readmission.
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.
Sarcopenia Assessment
Time Frame: Within 24 hours of admission and 24 hours before discharge through study completion, an average of 3 years
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.
Within 24 hours of admission and 24 hours before discharge through study completion, an average of 3 years
Sarcopenia Test
Time Frame: Within 24 hours of admission and 24 hours before discharge through study completion, an average of 3 years
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.
Within 24 hours of admission and 24 hours before discharge through study completion, an average of 3 years
Nutrition Assessment
Time Frame: Within 24 hours of admission and 24 hours before discharge through study completion, an average of 3 years
Intake compliance of food served (100%, 75%, 50%, 25% and 0% of the food served in each meal.
Within 24 hours of admission and 24 hours before discharge through study completion, an average of 3 years
Opioids Intake
Time Frame: Within 24 hours of admission and 24 hours before discharge through study completion, an average of 3 years
Quantity of morphine equivalent in mg per day at admission and at discharge
Within 24 hours of admission and 24 hours before discharge through study completion, an average of 3 years
Tumor Characteristics and Comorbidities
Time Frame: Within 24 hours of admission through study completion, an average of 3 years
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.
Within 24 hours of admission through study completion, an average of 3 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Vall d'Hebron Institute of Oncology

Collaborators

Hospital del Mar
Fundació Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau

Investigators

  • Principal Investigator: Oriol Mirallas, MD, Vall D'Hebron University Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

February 15, 2020

Primary Completion (Anticipated)

February 15, 2023

Study Completion (Anticipated)

March 15, 2024

Study Registration Dates

First Submitted

March 27, 2022

First Submitted That Met QC Criteria

September 5, 2022

First Posted (Actual)

September 9, 2022

Study Record Updates

Last Update Posted (Actual)

September 9, 2022

Last Update Submitted That Met QC Criteria

September 5, 2022

Last Verified

September 1, 2022

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • VHIO1601

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

IPD will be shared with all the participants of the study

IPD Sharing Time Frame

1 year

IPD Sharing Access Criteria

Principal investigators and sub-investigators

IPD Sharing Supporting Information Type

  • STUDY_PROTOCOL
  • ICF
  • CSR

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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