Arterial blood pressure correlates with 90-day mortality in sepsis patients

Kobayashi, Naoya; Nakagawa, Atsuhiro; Kudo, Daisuke; Ishigaki, Tsukasa; Ishizuka, Haruya; Saito, Kohji; Ejima, Yutaka; Wagatsuma, Toshihiro; Toyama, Hiroaki; Kawaguchi, Tomohiro; Niizuma, Kuniyasu; Ando, Kokichi; Kurotaki, Kenji; Kumagai, Michio; Kushimoto, Shigeki; Tominaga, Teiji; Yamauchi, M.

doi:10.1097/mbp.0000000000000398

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…Blood pressure measurements that exceeded the boundaries of reasonable physiological signals and damped waveforms were excluded. Both IBP and NIBP were limited to the following range: mean arterial pressure (MAP) [20,200] mmHg, systolic blood pressure (SBP) [40, 250] mmHg, and diastolic blood pressure (DBP) [10,150] mmHg. In addition, measurements with MAP higher than SBP or lower than DBP were excluded.…”

Section: Measurement and Criteria For Selection Of Ibp And Nibpmentioning

confidence: 99%

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A comparison of invasive arterial blood pressure measurement with oscillometric non-invasive blood pressure measurement in patients with sepsis

Jiang,

Li,

Wang

et al. 2024

J Anesth

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BackgroundMultiple studies had investigated the discrepancies of different blood pressure monitoring techniques in critical patients, but it remained controversial in patients with sepsis. This study aimed to compare noninvasive oscillometric blood pressure (NIBP) measurement with invasive arterial blood pressure (IBP) measurement in patients with sepsis. MethodsWe conducted a retrospective study to evaluate the agreement between IBP and NIBP using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Paired blood pressure measurements of mean arterial pressure (MAP), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were compared using Bland-Altman analysis and paired Student's t-test. We also focus on the effect of NE on the agreement between the two methods and the association between blood pressure and mortality during intensive care unit (ICU) stay. ResultsA total of 96,673 paired blood pressure measurements from 6,060 unique patients were analyzed in the study. In Bland-Altman analysis, the bias (± SD, 95% limits of agreement) was 6.21mmHg (± 12.05mmHg, -17.41 to 29.83mmHg) for MAP, 0.39mmHg (± 19.25mmHg, for SBP, and 0.80mmHg (± 12.92mmHg, -24.52 to 26.12mmHg) for DBP between the two techniques. Similarly, large limits of agreement were shown in different groups of NE doses. NE doses signi cantly affected the agreement between IBP and NIBP. SBP between the two methods gave an inconsistent assessment of patients' risk of ICU mortality. ConclusionIBP and NIBP were not interchangeable in septic patients. Clinicians should be aware that non-invasive MAP was clinically and signi cantly underestimated invasive MAP.

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Section: Measurement and Criteria For Selection Of Ibp And Nibpmentioning

confidence: 99%

“…et al revealed that mortality of MAP between 75 and 85 was signi cantly lower compared with those between 65 and 75 in patients with hypertension [9]. High-frequency arterial blood pressure data could be useful in predicting the clinical outcomes of patients with sepsis [10].…”

Section: Introductionmentioning

confidence: 99%

A comparison of invasive arterial blood pressure measurement with oscillometric non-invasive blood pressure measurement in patients with sepsis

Jiang,

Li,

Wang

et al. 2024

J Anesth

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Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study

Kobayashi

Shiga

Ikumi

et al. 2021

Sci Rep

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Monitoring the pain intensity in critically ill patients is crucial because intense pain can cause adverse events, including poor survival rates; however, continuous pain evaluation is difficult. Vital signs have traditionally been considered ineffective in pain assessment; nevertheless, the use of machine learning may automate pain assessment using vital signs. This retrospective observational study was performed at a university hospital in Sendai, Japan. Objective pain assessments were performed in eligible patients using the Critical-Care Pain Observation Tool (CPOT). Three machine-learning methods—random forest (RF), support vector machine (SVM), and logistic regression (LR)—were employed to predict pain using parameters, such as vital signs, age group, and sedation levels. Prediction accuracy was calculated as the harmonic mean of sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). Furthermore, 117,190 CPOT assessments were performed in 11,507 eligible patients (median age: 65 years; 58.0% males). We found that pain prediction was possible with all three machine-learning methods. RF demonstrated the highest AUROC for the test data (RF: 0.853, SVM: 0.823, and LR: 0.787). With this method, pain can be objectively, continuously, and semi-automatically evaluated in critically ill patients.

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Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest)

Zhang

et al. 2022

BMC Emerg Med

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Background Elderly patients with sepsis have many comorbidities, and the clinical reaction is not obvious. Thus, clinical treatment is difficult. We planned to use the laboratory test results and comorbidities of elderly patients with sepsis from a large-scale public database Medical Information Mart for Intensive Care (MIMIC) IV to build a random survival forest (RSF) model and to evaluate the model’s predictive value for these patients. Methods Clinical information of elderly patients with sepsis in MIMIC IV database was collected retrospectively. Machine learning (RSF) was used to select the top 30 variables in the training cohort to build the final RSF model. The model was compared with the traditional scoring systems SOFA, SAPSII, and APSIII. The performance of the model was evaluated by C index and calibration curve. Results A total of 6,503 patients were enrolled in the study. The top 30 important variables screened by RSF were used to construct the final RSF model. The new model provided a better C-index (0.731 in the validation cohort). The calibration curve described the agreement between the predicted probability of RSF model and the observed 30-day survival. Conclusions We constructed a prognostic model to predict a 30-day mortality risk in elderly patients with sepsis based on machine learning (RSF algorithm), and it proved superior to the traditional scoring systems. The risk factors affecting the patients were also ranked. In addition to the common risk factors of vasopressors, ventilator use, and urine output. Newly added factors such as RDW, type of ICU unit, malignant cancer, and metastatic solid tumor also significantly influence prognosis.

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Arterial blood pressure correlates with 90-day mortality in sepsis patients

Cited by 5 publications

References 37 publications

A comparison of invasive arterial blood pressure measurement with oscillometric non-invasive blood pressure measurement in patients with sepsis

A comparison of invasive arterial blood pressure measurement with oscillometric non-invasive blood pressure measurement in patients with sepsis

Semi-automated tracking of pain in critical care patients using artificial intelligence: a retrospective observational study

Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest)

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