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Deciphering the explanatory potential of blood pressure variables on post-operative length of stay through hierarchical clustering: A retrospective monocentric study

Cite this dataset

Cartaillet, Jérôme et al. (2024). Deciphering the explanatory potential of blood pressure variables on post-operative length of stay through hierarchical clustering: A retrospective monocentric study [Dataset]. Dryad. https://doi.org/10.5061/dryad.12jm63z5r

Abstract

Objective: Mean arterial pressure is widely used as the variable to monitor during anesthesia. But there are many other variables proposed to define intraoperative arterial hypotension. The goal of the present study was to search arterial pressure variables linked with prolonged postoperative length of stay (pLOS).

Design: Retrospective cohort study of adult patients having received general  for a scheduled non cardiac surgical procedure between 15th July 2017 and 31st December 2019.

Methods: pLOS was defined as a stay longer than the median (main outcome), adjusted for surgery type and duration. 330 arterial pressure variables were analyzed and organized through a clustering approach. An unsupervised hierarchical aggregation method for optimal cluster determination, employing Kendall’s tau coefficients and a penalized Bayes information criterion was used. Variables were ranked using the absolute standardized mean distance (aSMD) to measure their effect on pLOS. Finally, after multivariate independence analysis, the number of variables was reduced to three.

Results: Our study examined 9,516 patients. When LOS is defined as strictly greater than the median, 34% of patients experienced pLOS. Key arterial pressure variables linked with this definition of pLOS included the difference between the highest and lowest pulse pressure values computed throughout the surgery (aSMD[95%CI] =0.39[0.31-0.40], p<0.001), the accumulated time pulse pressure above 61mmHg (aSMD = 0.21[0.17-0.25], p<0.001), and the lowest MAP during surgery (aSMD= 0.20[0.16-0.24], p<0.001).

Conclusions: By applying a clustering approach, three arterial pressure variables were associated with pLOS. This scalable method can be applied to various dichotomized outcomes.

README: Deciphering the explanatory potential of blood pressure variables on post-operative length of stay through hierarchical clustering: A retrospective monocentric study

https://doi.org/10.5061/dryad.12jm63z5r

Demography.csv (on Zenodo)

Demography table contains main patients' characteristics. 10 variables

id_data Patient number
surgery 1: general; 2: lung; 3:neurosurgery; 4: urology; 5: vascular; 6: gynecology; 7: ENT
age 1: below 65 years; 2: 65 years and more
sex 1:M; 2: F
los Length of post-operative stay (days)
time_quartile Quartile of surgery duration (absolute number)
median_Stay Median length of stay (days)
Q3_Stay Third quartile of length of stay (days)
Q90_Stay 90th percentile of length of stay (days)
Short_IQR Interquartile range of length of stay <=1 (Q3-Q1) (days)
Measurements.csv (on Dryad)

Measurements table contains all variables related to arterial pressure and used for statistical analysis. 330 variables

Min_MAP minimal value of mean arterial pressure (mmHg)
Max_MAP maximal value of mean arterial pressure (mmHg)
Delta_MAP Largest Drop in MAP during surgery (mmHg)
Mean_MAP Mean MAP (mmHg)
Median_MAP Median MAP (mmHg)
std_MAP standard deviation of MAP (mmHg)
Var_MAP MAP variability (absolute number)
Cum_time_MAP_[XX] cumulative time of mean arterial pressure below [XX] mmHg (minutes)
Area_time_MAP_[XX] cumulative area of mean arterial pressure below [XX] mmHg  (absolute number)
Min_PP minimal value of pulse pressure (mmHg)
Max_PP maximal value of pulse pressure (mmHg)
Delta_PP Largest Drop in PP during surgery (mmHg)
Mean_PP Mean PP  (mmHg)
Median_PP Median PP  (mmHg)
std_PP standard deviation of PP  (mmHg)
Var_PP PP variability  (absolute number)
Cum_time_PP_[XX] cumulative time of pulse pressure below [XX] mmHg (minutes)
Cum_area_PP_[XX] cumulative area of pulse pressure below [XX] mmHg (absolute number)
Min_S minimal value of systolic arterial pressure (mmHg)
Max_S maximal value of systolic arterial pressure (mmHg)
Delta_S Largest Drop in S during surgery  (mmHg)
Mean_S Mean S  (mmHg)
Median_S Median S (mmHg)
std_S standard deviation of S (mmHg)
Var_S S variability (absolute number)
Cum_time_S_[XX] cumulative time of systolic arterial pressure below [XX] mmHg (minutes) 
Area_time_S_[XX] cumulative area of systolic arterial pressure below [XX] mmHg (absolute number)
Min_D minimal value of diastolic arterial pressure (mmHg)
Max_D maximal value of diastolic arterial pressure (mmHg)
Delta_D Largest Drop in diastolic arterial during surgery (mmHg)
Mean_D Mean diastolic arterial (mmHg)
Median_D Median diastolic arterial (mmHg)
std_D standard deviation of diastolic arterial (mmHg)
Var_D diastolic arterial variability (absolute number)
Cum_time_D_[XX] cumulative time of diastolic pressure below [XX] mmHg (minutes)
Area_time_D_[XX] cumulative area of diastolic pressure below [XX] mmHg (absolute number)

Methods

Patient characteristics and preoperative medications were collected from Cesare™, a computerized software for preoperative anesthetic evaluation (Bow Médical, 80440 Boves, France). Centricity Anesthesia software was used to collect intraoperative variables (GE Healthcare, 78 530 Buc, France). lenght of stay and in-hospital mortality were obtained by questioning the health data warehouse.