Neoplasms

Respiratory Tract (Lung and Bronchial) Diseases

All Conditions

Symptoms and General Pathology

Carcinoma, Non-Small-Cell Lung

Neoplasm Metastasis

Lung Neoplasms

Carcinoma

Respiratory Tract Neoplasms

Thoracic Neoplasms

Lung Diseases

Respiratory Tract Diseases

Carcinoma, Bronchogenic

Bronchial Neoplasms

Neoplastic Processes

For NSCLC spinal metastasis patients with 0-3 of novel survival prediction algorithm.

For NSCLC spinal metastasis patients with 4-6 of novel survival prediction algorithm.

For NSCLC spinal metastasis patients with 7-10 of novel survival prediction algorithm.

Yuhui Shen, Ph.D., M.D.

Weibin Zhang, Ph.D., M.D.

Shizhao Zang, M.D.

Qiyuan Bao, Ph.D., M.D.

The purpose of this study is to learn whether our own made predictive algorithm can be used as a clinical practical decision support for patients with NSCLC spinal metastasis. The scoring system consists of the use of EGFR-TKI, KPS, Age, SCC, CA125 and smoking history. By predicting survival doctors could determine which patients are suitable for palliative therapy.

Investigators have performed a retrospective study on 176 patients with NSCLC spinal metastasis under the oversight of hospital's ethics committee, and investigators found that the use of EGFR-TKI, KPS, Age, SCC, CA125 and smoking history had significant association with survival. Then investigators built a simple, easy to use scoring system based on the features mentioned above. The score was calculated as 1 (for patients didn't receive EGFR-TKI), +2 (for KPS \<50%), +1 (for KPS 50-70%), +1 (Age \>60years), 2 (SCC ≥1.5ng/ml), +3 (CA125 ≥35 U/ml), +1 (smoking history 1-10/day), +2 (smoking history \>10/day), and 0 otherwise. This algorithm was used to divide the patients into low risk (0-3), intermediate risk (4-6), high risk groups (7-10) to predict survival and determine which patients are suitable for palliative therapy. Now investigators wish to register this study to do a further research, in order to verify the accuracy and sensitivity of this algorithm.

Verification of Novel Survival Prediction Algorithm for Patients With NSCLC Spinal Metastasis

An Observational Study of Novel Survival Prediction Algorithm as Clinical Decision Support for Patients With Non-Small-Cell Lung Cancer (NSCLC) Spinal Metastasis

Accuracy and sensitivity of novel survival prediction algorithm derived from differences between the predicted and actual survival of NSCLC spinal metastasis patients from 3 different risk groups.

Differences in the primary and metastatic lesions after therapy according to NCCN suggestion of NSCLC spinal metastasis patients from 3 different risk groups.

Differences in the serum markers after therapy according to NCCN suggestion of NSCLC spinal metastasis patients from 3 different risk groups.

The correlation between visceral metastasis and overall survival (OS) of NSCLC spinal metastasis patients

Assessment of pain level (1-10, higher value represents more pain)

Assessment of quality of life (22-88, higher value represents worse quality of life)

Assessment of neurological function (A-E, higher value represents better function)

Assessment of spinal instability (0-18, higher value represents worse instability)

Ruijin Hospital

Associate Chief Physician of Orthopaedics

Patients referred to the oncology / radiation oncology departments for palliative therapy.

There are no specific interventions in this observational study

no specific interventions

Stephan Gripp, Dr.

The accuracy of clinical survival estimates is tested in comparison to objective prognostic scores.

* At presentation a clinical survival estimate is obtained in each patient.
* Survival is also estimated using published prognostic scores in palliative care.
* Prognostic scores based on blood samples, general symptoms, complaints, and performance status are calculated and validated, respectively
* Each patient completes questionnaires on psychological distress
* Survival estimates are limited to 6 months.
* Each patient is followed for at least 6 months.

Prognosis and Survival Estimates in Palliative Therapy

Prognosis in Palliative Therapy - a Prospective Study on Survival Prognostication in Advanced Stage Cancer Patients

Heinrich-Heine University, Duesseldorf

Behaviors and Mental Disorders

Quality of Life

Jen-Hsuan Liu, MD

Jaw-Shiun Tsai, MDPHD

Jenhsuan Liu

This study is going to use wearable devices and smartphones to collect physical data from terminal patients and build a survival predicting model for terminal patients with machine learning. Investigators hypothesize that continuous physical data monitoring could offer a hint to better predictability in end-of-life care.

The study aim to examine the feasibility of utilizing wearable devices and smartphones in palliative patients in Taiwan. In addition, investigators try to identify the relationship between mobile health data and disease progression and establish a predicting model to the emergent medical need and death of patients, via machine learning.

This is a single-arm observational study using wearable devices and smartphones in terminal cancer patients. Investigators planned to enroll 75 patients who receive palliative care. After obtaining consent from the patients or their legally authorized surrogate decision-makers, a baseline assessment will be conducted, with a guide to use wearable devices and phone apps.

Investigators will keep regular follow-up for 52 weeks or until the participants' death. Assessment will be conducted every week, face-to-face or by telephone contact. A routine assessment includes symptoms and functionality in the past week, and vital signs and facial photograph will be recorded if possible. Physical data measured from wearable devices would be recorded continuously. The emergent medical needs of patient, including emergency department visit, unplanned admission and death of participants will be recorded if happen.

The primary outcome is the predictive performance (sensitivity and specificity) of the machine-learning model using wearable device data and symptoms assessment. The secondary outcomes are symptoms, including pain, dyspnea, diarrhea, constipation, nausea, vomiting, insomnia, depression, anxiety and fatigue. Users' opinion and comment to using experience will also be recorded.

Using Wearable Device to Improve Quality of Palliative Care

Using Wearable Device and Smart Phone to Improve Survival Prediction and Quality of Life in Patients Receiving Palliative Care

Palliative performance scale (PPS) will be regularly assessed during the follow-up. The AUC-ROC of using PPS for survival prediction will be calculated and compared with the machine-learning model.

Glasgow Prognostic Score (GPS) will be assessed if C-reactive protein (CRP) and albumin are examined during the follow-up. The AUC-ROC using GPS for survival prediction will be calculated and compared with the machine-learning model.

Palliative Prognostic Index(PPI) will be regularly assessed during the follow-up. The AUC-ROC of using PPI for survival prediction will be calculated and compared with the machine-learning model.

Palliative Prognostic Score (PaP) will be assessed if laboratory data available during the follow-up. The AUC-ROC of using PaP for survival prediction will be compared with the machine-learning model.

If unexpected medical needs happen, investigators will record time spent at ER stay or hospital admission

Investigators will record duration between events (death, unexpected medical needs, admission and discharge) or duration from enrollment to events, if they happen

Investigators will record the overall survival and survival time from enrollment.

If patient died during the follow-up, investigator will record the site of death (at home or any other chosen place, in the hospital or ER). Other details will be recorded if the family or caregivers are willing to provide.

Investigator will ask and record any discomfort or side effect noted during the follow-up and at the end of the study. Investigator will survey for user experience of patients or caregivers at the end of the study.

Personal background such as educational level, age, and previous use of technological product will be recorded. Investigator will explore the relation between these factors and the user experience.

Measured data from wearable device and regular assessment (including medical condition, laboratory data, symptom, functional assessment) will be integrated to build one machine-learning model to predict patients' death or survival within specific time range. The primary outcome is to evaluate the Area Under the Receiver Operating Characteristic curve (AUC - ROC) of the machine-learning model in predicting patients' survival.

Measured data from wearable device and regular assessment (including medical condition, laboratory data, symptom, functional assessment) will be integrated to build one machine-learning model to predict patient's unexpected medical needs (which is defined as emergency department visit or unplanned admission to hospital).

The primary outcome is to evaluate Area Under the Receiver Operating Characteristic curve (AUC-ROC) of the machine-learning model in predicting unexpected medical needs.

The severity of symptoms will be recorded by symptoms assessment scale (SAS). Investigators will explore the correlation between the wearable device parameters and symptoms.

The functional status will be assessed by Australia-modified Karnofsky Performance Status (AKPS) during the follow-up. Investigators will explore the correlation between AKPS and wearable device parameters

Evaluation of palliative care phases from the Palliative Care Outcomes Collaboration (PCOC) system will be assessed regularly. Investigators will explore the correlation between the palliative care phases and other parameters (wearable device parameters, symptoms, medical condition).

National Taiwan University

National Taiwan University Hospital

Shabbir Syed-Abdul, PhD

Shabbir Syed Abdul

The purpose of this study is to develop a novel deep-learning-based survival prediction model employing patient activity data recorded by a wearable device.

This study aims to develop a deep-learning-based survival prediction model that utilizes patient movement data upon admission to predict their clinical outcomes: either death or discharge with stable condition. Objective data of the patients are recorded by a wearable device and documented as parameters of physical activity, angle, and spin. In addition to objective data, the investigators also document patients' Karnofsky Performance Status assessed subjectively by clinical doctors. Finally, the investigators aim to explore and describe the applicability, potential, and limitations of the survival prediction model based on patient movement data as a simple prognostic parameter in clinical settings.

Artificial Intelligence-based Mortality Prediction Among Cancer Patients in the Hospice Ward

Artificial Intelligence-based Activity Recognition and Mortality Prediction Using Circadian Rhythm, Among Cancer Patients in the Hospice Ward

The primary outcome of the study will be to evaluate whether the analysis of the movement data captured using actigraphy device can help to predict clinical outcomes either deceased or discharged alive from hospital, with a high specificity and sensitivity, using Artificial Intelligence based prediction modelling.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Verification of Novel Survival Prediction Algorithm for Patients With NSCLC Spinal Metastasis	NCT03363685	Spinal Metastas... Non-Small-Cell ...		18 Years - 75 Years	Ruijin Hospital
Prognosis and Survival Estimates in Palliative Therapy	NCT00871520	Cancer	no specific int...	18 Years -	Heinrich-Heine University, Duesseldorf
Using Wearable Device to Improve Quality of Palliative Care	NCT05054907	Terminal Cancer End Stage Cance...		20 Years - 105 Years	National Taiwan University Hospital
Artificial Intelligence-based Mortality Prediction Among Cancer Patients in the Hospice Ward	NCT04883879	End Stage Cance...		20 Years -	Taipei Medical University

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