The Japanese Intensive care PAtient Database (JIPAD): A national intensive care unit registry in Japan

Irie, Hiromasa; Okamoto, Hiroshi; Uchino, Shigehiko; Endo, Hideki; Uchida, Masatoshi; Kawasaki, Tatsuya; Kumasawa, Junji; Tagami, Takashi; Shigemitsu, Hidenobu; Hashiba, Eiji; Aoki, Yoshitaka; Kurosawa, Hiroshi; Hatakeyama, Junji; Ichihara, Nao; Hashimoto, Satoru; Nishimura, Masaji

doi:10.1016/j.jcrc.2019.09.004

Cited by 54 publications

(37 citation statements)

References 25 publications

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“…Approximately half the patients were in the ICU for at least three nights after elective surgery. Based on the Japanese intensive care patients’ database registration [ 39 ], the proportion of the ICU population admitted to the ICU for planned surgery in Japan was greater than that in Australia and New Zealand [ 40 ], the United Kingdom [ 41 ], and Brazil [ 42 ]. Moreover, our study included only ICU survivors living at home, which probably led to an increase in the proportion of elective surgery patients.…”

Section: Discussionmentioning

confidence: 99%

Prevalence of and risk factors for post-intensive care syndrome: Multicenter study of patients living at home after treatment in 12 Japanese intensive care units, SMAP-HoPe study

et al. 2021

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Few studies have examined the epidemiology of post-intensive care syndrome in Japan. This study investigated the mental health and quality of life of patients living at home in Japan after intensive care unit (ICU) discharge. Additionally, we examined whether unplanned admission to the ICU was associated with more severe post-traumatic stress disorder (PTSD), anxiety, and depressive symptoms. An ambidirectional cohort study was conducted at 12 ICUs in Japan. Patients who stayed in the ICU for > 3 nights and were living at home for 1 year afterward were included. One year after ICU discharge, we retrospectively screened patients and performed a mail survey on a monthly basis, including the Impact of Event Scale—Revised (IER-S), the Hospital Anxiety Depression Scale (HADS), and the EuroQOL—5 Dimension (EQ-5D-L) questionnaires. Patients’ characteristics, delirium and coma status, drugs used, and ICU and hospital length of stay were assessed from medical records. Descriptive statistics and multilevel linear regression modeling were used to examine our hypothesis. Among 7,030 discharged patients, 854 patients were surveyed by mail. Of these, 778 patients responded (response rate = 91.1%). The data from 754 patients were analyzed. The median IES-R score was 3 (interquartile range [IQR] = 1‒9), and the prevalence of suspected PTSD was 6.0%. The median HADS anxiety score was 4.00 (IQR = 1.17‒6.00), and the prevalence of anxiety was 16.6%. The median HADS depression score was 5 (IQR = 2‒8), and the prevalence of depression was 28.1%. EQ-5D-L scores were lower in our participants than in the sex- and age-matched Japanese population. Unplanned admission was an independent risk factor for more severe PTSD, anxiety, and depressive symptoms. Approximately one-third of patients in the general ICU population experienced mental health issues one year after ICU discharge. Unplanned admission was an independent predictor for more severe PTSD symptoms.

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Section: Discussionmentioning

confidence: 99%

Prevalence of and risk factors for post-intensive care syndrome: Multicenter study of patients living at home after treatment in 12 Japanese intensive care units, SMAP-HoPe study

et al. 2021

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“…Because the discrimination of the APACHE III-j model has been shown to be excellent in the Japanese ICU population [6], our focus in developing a new model was to improve the calibration of the APACHE III-j model. The predicted mortality needed to be reduced.…”

Section: Recalibration Methodsmentioning

confidence: 99%

“…Participating ICUs submit data on all patients admitted to their ICUs in each fiscal year. More details on the JIPAD can be found elsewhere [6].…”

Section: Data Collectionmentioning

confidence: 99%

“…The APACHE III-j is a risk prediction model for inhospital mortality that is widely used in Japanese ICUs. However, APACHE III-j overestimated the risk of death for Japanese ICU patients, producing estimates that are more than two times the observed mortality [6]. It is imperative to have an accurate risk prediction model to appraise individual risk of death in daily clinical practice and evaluate the overall quality of care in an ICU using quality metrics such as the standardized mortality ratio (SMR), a ratio of observed mortality to expected mortality [7].…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of the predictive risk of death model for adult patients admitted to intensive care units in Japan: an approach to improve the accuracy of healthcare quality measures

et al. 2021

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Background The Acute Physiology and Chronic Health Evaluation (APACHE) III-j model is widely used to predict mortality in Japanese intensive care units (ICUs). Although the model’s discrimination is excellent, its calibration is poor. APACHE III-j overestimates the risk of death, making its evaluation of healthcare quality inaccurate. This study aimed to improve the calibration of the model and develop a Japan Risk of Death (JROD) model for benchmarking purposes. Methods A retrospective analysis was conducted using a national clinical registry of ICU patients in Japan. Adult patients admitted to an ICU between April 1, 2018, and March 31, 2019, were included. The APACHE III-j model was recalibrated with the following models: Model 1, predicting mortality with an offset variable for the linear predictor of the APACHE III-j model using a generalized linear model; model 2, predicting mortality with the linear predictor of the APACHE III-j model using a generalized linear model; and model 3, predicting mortality with the linear predictor of the APACHE III-j model using a hierarchical generalized additive model. Model performance was assessed with the area under the receiver operating characteristic curve (AUROC), the Brier score, and the modified Hosmer–Lemeshow test. To confirm model applicability to evaluating quality of care, funnel plots of the standardized mortality ratio and exponentially weighted moving average (EWMA) charts for mortality were drawn. Results In total, 33,557 patients from 44 ICUs were included in the study population. ICU mortality was 3.8%, and hospital mortality was 8.1%. The AUROC, Brier score, and modified Hosmer–Lemeshow p value of the original model and models 1, 2, and 3 were 0.915, 0.062, and < .001; 0.915, 0.047, and < .001; 0.915, 0.047, and .002; and 0.917, 0.047, and .84, respectively. Except for model 3, the funnel plots showed overdispersion. The validity of the EWMA charts for the recalibrated models was determined by visual inspection. Conclusions Model 3 showed good performance and can be adopted as the JROD model for monitoring quality of care in an ICU, although further investigation of the clinical validity of outlier detection is required. This update method may also be useful in other settings.

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“…Continuous physiological monitoring has created high-volume critical care databases, offering substratum for machine learning (ML). Some countries have been developing their own databases in the recent years, 2,3,4,5 and chances are others will follow in the same direction very soon. The first resource of the kind was the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC), 6 maintained by the Massachusetts Institute of Technology Laboratory for Computational Physiology.…”

Section: Big Data and Machine Learning: An Overviewmentioning

confidence: 99%

Artificial intelligence: what should an intensivist have in mind in the beginning of the new era?

Filho¹

2021

APIC

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A new era is coming for medicine, and for critical care in particular. The intensive care unit is at the edge of being completely changed by artificial intelligence, and many challenges are ahead of the intensive care physician. This article aims to address the benefits and difficulties that big data will bring to clinicians, and to provide an overview on the subject. Key words: Big Data; Artificial intelligence; ICU; Critical Care; Black box Citation: Filho LACB. Artificial intelligence: what should an intensivist have in mind in the beginning of the new era. Anaesth. Pain intensive care 2021;25(1):8-12. DOI: 10.35975/apic.v25i1.1428 Received: 10 December 2020, Reviewed: 3 January 2021, Accepted: 8 January 2021

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The Japanese Intensive care PAtient Database (JIPAD): A national intensive care unit registry in Japan

Cited by 54 publications

References 25 publications

Prevalence of and risk factors for post-intensive care syndrome: Multicenter study of patients living at home after treatment in 12 Japanese intensive care units, SMAP-HoPe study

Prevalence of and risk factors for post-intensive care syndrome: Multicenter study of patients living at home after treatment in 12 Japanese intensive care units, SMAP-HoPe study

Development and validation of the predictive risk of death model for adult patients admitted to intensive care units in Japan: an approach to improve the accuracy of healthcare quality measures

Artificial intelligence: what should an intensivist have in mind in the beginning of the new era?

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