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

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

Official Title
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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

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

Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Predict Mortality | Mortality at 30-day after discharge | 30 days after discharge | 
| Predict hospital length of stay | 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 | 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) | EORTC QLQ-C30 Minimum: 30 Maximum: 126. More punctuation, worse QoL | Within 24 hours of admission | 
| 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. | 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 | 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 | 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 | 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 | 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 | 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Hospitalization, Oncologic patients