UMIN-CTR Clinical Trial

Basic information
Public title	Multidisciplinary Observational Study with Artificial Intelligence on Diagnosis, Prognosis Prediction, and Treatment of Pneumonia for the Older Adults
Acronym	Multidisciplinary Observational Study with Artificial Intelligence on Diagnosis, Prognosis Prediction, and Treatment of Pneumonia for the Older Adults
Scientific Title	Multidisciplinary Observational Study with Artificial Intelligence on Diagnosis, Prognosis Prediction, and Treatment of Pneumonia for the Older Adults
Scientific Title:Acronym	Multidisciplinary Observational Study with Artificial Intelligence on Diagnosis, Prognosis Prediction, and Treatment of Pneumonia for the Older Adults
Region	Japan

Condition
Condition	pneumonia
Classification by specialty	Medicine in general Pneumology Geriatrics Rehabilitation medicine Adult
Classification by malignancy	Others
Genomic information	NO

Objectives
Narrative objectives1	The primary objective of this study is to verify whether artificial intelligence analysis can predict life expectancy and optimize treatment for older patients with pneumonia (including COVID-19) based on the information collected in the electronic medical record (EMR) .
Basic objectives2	Others
Basic objectives -Others	The secondary objective of this study is to use artificial intelligence analysis to identify factors related to pneumonia treatment and physical and cognitive function prognosis in older patients with pneumonia (incl. COVID-19).
Trial characteristics_1
Trial characteristics_2
Developmental phase

Assessment
Primary outcomes	Severity (use of ventilator/noninvasive positive pressure ventilation with or without oxygen) and prognosis (survival and death of patients with pneumonia) 30 days after onset of pneumonia
Key secondary outcomes	(1) Clinical characteristics of causative organisms (2) Clinical response to treatment according to the type of antimicrobial agent (body temperature chart and blood sample results to determine whether the patient is cured) (3) Length of hospitalization (days) (4) Functional prognosis (alternative nutrition, type of walking aids, cognitive symptoms, facility admission) (5) Sarcopenia and osteoporosis (dual energy X-ray absorptiometry DEXA, bioimpedance method BIA) (6) Agreement between artificial intelligence (AI) and physician prognosis (primary endpoint, functional prognosis) (7) AI assisted diagnosis of chest X-ray

Base
Study type	Observational

Study design
Basic design
Randomization
Randomization unit
Blinding
Control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment

Intervention
No. of arms
Purpose of intervention
Type of intervention
Interventions/Control_1
Interventions/Control_2
Interventions/Control_3
Interventions/Control_4
Interventions/Control_5
Interventions/Control_6
Interventions/Control_7
Interventions/Control_8
Interventions/Control_9
Interventions/Control_10

Eligibility
Age-lower limit	65 years-old <=
Age-upper limit	120 years-old >
Gender	Male and Female
Key inclusion criteria	Pneumonia group (1) Patients with a history of pneumonia in the past (2) Those who are 65 years of age or older at the time of onset of pneumonia. (3) Those who goes to hospital regularly or day-care center. Non-pneumonia group (1) Those who have no history of pneumonia in the past. (2) Patients who are 65 years old or older at the time of their last visit to a hospital or nursing care facility. (3) Those who goes to hospital regularly or day-care center.
Key exclusion criteria	(1) Persons who refuse to participate in the research
Target sample size	9000

Research contact person
Name of lead principal investigator	1st name Naoto Middle name Last name Ozaki
Organization	The Jikei University School of Medicine
Division name	Department of Rehabilitation Medicine
Zip code	105-8471
Address	3-19-18 Nishi-Shinbashi, Minato-ku, Tokyo
TEL	03-3433-1111
Email	nozakiame@jikei.ac.jp

Public contact
Name of contact person	1st name Naoto Middle name Last name Ozaki
Organization	The Jikei University School of Medicine
Division name	Department of Rehabilitation Medicine
Zip code	105-8471
Address	3-19-18 Nishi-Shinbashi, Minato-ku, Tokyo
TEL	03-3433-1111
Homepage URL
Email	nozakiame@jikei.ac.jp

Sponsor
Institute	The Jikei University School of Medicine
Institute
Department

Funding Source
Organization	Japan Agency for Medical Research and Development
Organization
Division
Category of Funding Organization	Japanese Governmental office
Nationality of Funding Organization

Other related organizations
Co-sponsor
Name of secondary funder(s)

IRB Contact (For public release)
Organization	The Jikei University School of Medicine
Address	3-19-18 Nishi-Shinbashi, Minato-ku, Tokyo
Tel	03-3433-1111
Email	rinri@jikei.ac.jp

Secondary IDs
Secondary IDs	NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW

Institutions
Institutions	東京大学（東京都）,キッコーマン総合病院（千葉県）,国際医療福祉大学市川病院（千葉県）, 小張総合病院（千葉県）, 野田病院（千葉県）, 横浜国立大学（神奈川県）, 季美の森リハビリテーション病院（千葉県）, 日本IBM（東京都）

Other administrative information
Date of disclosure of the study information	2022 Year 12 Month 04 Day

Related information
URL releasing protocol
Publication of results	Unpublished

Result
URL related to results and publications
Number of participants that the trial has enrolled
Results
Results date posted
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
Participant flow
Adverse events
Outcome measures
Plan to share IPD
IPD sharing Plan description

Progress
Recruitment status	Open public recruiting
Date of protocol fixation	2022 Year 10 Month 25 Day
Date of IRB	2022 Year 10 Month 25 Day
Anticipated trial start date	2022 Year 10 Month 25 Day
Last follow-up date	2026 Year 07 Month 31 Day
Date of closure to data entry
Date trial data considered complete
Date analysis concluded

Other

Other related information

This is a multi-institutional observational study to examine whether artificial intelligence analysis of medical records can predict life expectancy, severity of illness, functional prognosis, and appropriate treatment recommendations in older patients with pneumonia. The following information is to be collected: Clinical information and laboratory results of pneumonia patients will be obtained from the electronic medical records of participating institutions. The clinical data will include basic information such as age, gender, height, weight, body temperature, race (place of birth of the patient and one's parents), community-acquired infection, nosocomial infection, past medical history, complications, length of hospitalization, blood data, therapeutic drugs, chest X-ray, CT, MRI, DEXA, BIA and other laboratory records, clinical response to treatment, vital sign(temperature, blood pressure, oxygenation, etc.) ,complications, severity of illness, whether or not a ventilator was used, invasive mechanical ventilation (with or without intubation), duration of intubation, frequency of use of vasopressors or renal replacement therapy, outcome (survival or death), presence of alternative nutrition (gastrostomy, central venous nutrition, etc.), duration of hospitalization, cognitive function assessment, muscle strength assessment (grip strength, knee extension muscle strength), gait function evaluation, long-term care insurance related records (attending physician's opinion, long-term care certification results), history of hospitalization for pneumonia, history of COVID-19, history of medical conditions (internal diseases, bone fracture, Parkinson's disease, cerebrovascular disease, dementia), number of hospitalizations, alternative nutrition, type of walking aid, whether the patient is institutionalized, etc.

Management information
Registered date	2022 Year 12 Month 04 Day
Last modified on	2022 Year 12 Month 05 Day

Link to view the page
URL(English)	https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000056591