Association between Mean Arterial Pressure during the First 24 Hours and Clinical Outcome in Critically Ill Stroke Patients: An Analysis of the MIMIC-III Database

Abstract

Abnormal blood pressure is common in critically ill stroke patients. However, the association between mean arterial pressure (MAP) and mortality of critically ill stroke patients remains unclear. We extracted eligible acute stroke patients from the MIMIC-III database. The patients were divided into three groups: a low MAP group (MAP ≤ 70 mmHg), a normal MAP group (70 mmHg < MAP ≤ 90 mmHg), and a high MAP group (MAP > 90 mmHg). The Cox proportional hazards model and restricted cubic splines were used to assess the association between MAP and mortality. Sensitivity analyses were conducted to investigate whether MAP had different effects on mortality in different subpopulations. A total of 2885 stroke patients were included in this study. The crude 7-day and 28-day mortality was significantly higher in the low MAP group than that in the normal MAP group. By contrast, patients in the high MAP group did not have higher crude 7-day and 28-day mortality than those in the normal MAP group. After multiple adjustments using the Cox regression model, patients with low MAP were consistently associated with higher 7-day and 28-day mortality than those with normal MAP in the following subgroups: age > 60 years, male, those with or without hypertension, those without diabetes, and those without CHD (p < 0.05), but patients with high MAP were not necessarily associated with higher 7-day and 28-day mortality after adjustments (most p > 0.05). Using the restricted cubic splines, an approximately L-shaped relationship was established between MAP and the 7-day and 28-day mortality in acute stroke patients. The findings were robust to multiple sensitivity analyses in stroke patients. In critically ill stroke patients, a low MAP significantly increased the 7-day and 28-day mortality, while a high MAP did not, suggesting that a low MAP is more harmful than a high MAP in critically ill stroke patients.

A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission

“…Daan et al reported that the restricted cubic splines regression (a nonlinear modeling methods) outperformed the logistic regression with linear terms when assessing the nonlinear relationship between continuous predictors and outcome [36]. Although some studies had fitted non-linear relationships between predictors and outcomes by using variables with cubic splines in logistic regression, the choice of location and number of knots was strongly influenced by a priori experience [9,36,37]. Taking the "maximum temperature" variable in this study as an example, we found that cubic spline regressions (RCS) using 3 knots (10th, 50th and 90th percentiles) and 5 knots (5th, 27.5th, 50th, 72.5th and 95th percentiles) showed significantly different trends in their curves after the "maximum temperature" above 38 degrees (results not reported).…”

“…Monitoring information during ICU was important to improve patient care and prognosis [4,5], and several studies have shown that changes in vital signs and laboratory indictors always precede the rapid deterioration of a patient's condition [6,7]. Similarly, the prognosis of stroke patients admitted to ICU was strongly influenced by the monitoring of various clinical features [8,9]. Jun Zhao et al found that abnormalities in inflammatory biomarkers such as neutrophil to lymphocyte ratio and platelet to lymphocyte ratio were associated with increased stroke mortality [10].…”

A novel higher performance nomogram based on explainable machine learning for predicting mortality risk in stroke patients within 30 days based on clinical features on the first day ICU admission

“…Daan et al reported that the restricted cubic splines regression (a nonlinear modeling methods) outperformed the logistic regression with linear terms when assessing the nonlinear relationship between continuous predictors and outcome [35]. Although some studies had tted non-linear relationships between predictors and outcomes by using variables with cubic splines in logistic regression, the choice of location and number of knots was strongly in uenced by a priori experience [9,35,36]. Taking the "temperature_max" variable in this study as an example, we found that cubic spline regressions (RCS) using 3 knots (10th, 50th and 90th percentiles) and 5 knots (5th, 27.5th, 50th, 72.5th and 95th percentiles) showed signi cantly different trends in their curves after the "temperature_max" above 38 degrees (results not reported).…”

“…Monitoring information during ICU was important to improve patient care and prognosis [4,5], and several studies have shown that changes in vital signs and laboratory indictors always precede the rapid deterioration of a patient's condition [6,7]. Similarly, the prognosis of stroke patients admitted to ICU was strongly in uenced by the monitoring of various clinical features [8,9]. Jun Zhao et al found that abnormalities in in ammatory biomarkers such as neutrophil to lymphocyte ratio and platelet to lymphocyte ratio were associated with increased stroke mortality [10].…”