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Table 2 Perioperative data (n = 699)

From: Can a deep learning model based on intraoperative time-series monitoring data predict post-hysterectomy quality of recovery?

Preoperative data
 Mean age ± SD, year 50 ± 7
 Mean body mass index ± SD, kg/m2 25 ± 3
 ASA physical status, no. (%)
  I 229 (32.8)
  II 470 (67.2)
 Coexisting medical condition, no. (%)
  Psychiatric disease 3 (0.4)
  Neurological disease 15 (2.1)
  Hypertension 142 (20.3)
  Cardiovascular disease 26 (3.7)
  Pulmonary disease 8 (1.1)
  Endocrinological disease 69 (9.9)
  Renal insufficiency 2 (0.3)
  Digestive disease 22 (3.1)
 History of anesthesia, no. (%)
  Never 286 (40.9)
  General anesthesia 197 (28.2)
  Spinal anesthesia 182 (26.0)
  Nerve block 2 (0.3)
  Local anesthesia 57 (8.2)
 History of PONV, no. (%)
  Never had surgery 279 (39.9)
  Surgery without PONV 377 (53.9)
  Surgery with PONV 43 (6.2)
 History of motion sickness, no. (%) 154 (22.0)
 Mean hemoglobin ± SD, g/l 123 ± 18
 Mean hematocrit ± SD, % 37 ± 5
 Mean creatinine ± SD, μmol/l 59 ± 13
Intraoperative intervention data, mean ± SD
 Duration of anesthesia, min 175 ± 74
 Propofol, mg 958 ± 437
 Remifentanil, mg 1.2 ± 0.7
 Sufentanil, mcg 32 ± 15
 Crystalloid, ml 1512 ± 575
 Estimated blood loss, ml 69 ± 93
 Urine output, ml 369 ± 261
Intraoperative monitoring data, mean ± SDa
 Mean respiratory rate, breath per min 14 ± 2
 Mean end-tidal carbon dioxide, mmHg 34 ± 4
 Mean heart rate, beat per min 66 ± 9
 Mean systolic blood pressure, mmHg 117 ± 12
 Mean diastolic blood pressure, mmHg 73 ± 8
 Mean MAP, mmHg 86 ± 9
 Mean pulse oxygen saturation, % 100 ± 1
 Mean body temperature, °C 36 ± 1
 Mean muscular tissue oxygen saturation, % 83 ± 7
Postoperative QoR
 QoR-15 score, mean ± SD 121 ± 19
 QoR-15 score, median [IQR] 122 [109-135]
 Number of patients with a QoR-15 ≥122, no. (%) 354 (50.6)
  1. SD standard deviation, ASA American Society of Anesthesiologists, PONV postoperative nausea and vomiting, MAP mean arterial pressure, QoR quality of recovery, IQR interquartile range
  2. aFor time-series data, we first removed those outliers defined as the data outside of the 0.5th–99.5th percentile. The mean of all data within the 0.5th–99.5th percentile was first derived for each patient. These means were then averaged to derive the mean for all patients
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