Intraoperative Hemorrhagic Shock in Cancer Surgical Patients: Short and Long-Term Mortality and Associated Factors

Factors Associated with 1-Year Mortality in Elderly Patients (Age ≥ 80 Years) with Cancer Undergoing Major Abdominal Surgery: A Retrospective Cohort Study

BACKGROUND

The number of elderly patients undergoing major abdominal surgery is increasing, but the factors affecting their postoperative outcomes remain unclear. This study aimed to identify the factors associated with 1-year mortality among elderly patients (age ≥ 80 years) with cancer undergoing major abdominal surgery.

METHODS

This retrospective cohort study was conducted from March 2009 to December 2020. The study enrolled 378 patients 80 years old or older who underwent major abdominal surgery. The main outcome was 1-year mortality, and the factors associated with mortality were analyzed.

RESULTS

Of the 378 patients, 92 died at 1 year (24.3%), whereas the 30-day mortality rate was 4% (n = 15). In the multivariate analysis, the factors independently associated with 1-year mortality were preoperative Eastern Cooperative Oncology Group (ECOG) performance status (PS) score higher than 1 (odds ratio [OR], 3.189; 95% confidence interval [CI], 1.595-6.377; p = 0.001), preoperative weight loss greater than 3 kg (OR, 2.145; 95% CI, 1.044-4.404; p = 0.038), use of an intraoperative vasopressor (OR, 3.090; 95% CI, 1.188-8.042; p = 0.021), and postoperative red blood cell units (OR, 1.212; 95% CI, 1.045-1.405; p = 0.011). Survival was associated with perioperative management according to an enhanced recovery after surgery (ERAS) protocol (OR, 0.370; 95% CI, 0.160-0.854; p = 0.006) and supramesocolic surgery (OR, 0.371; 95% CI, 0.158-0.871; p = 0.023).

CONCLUSION

The study identified several factors associated with an encouraging 1-year mortality rate in this setting. These results highlight the need for identification of suitable targets to optimize pre-, intra-, and postoperative management in order to improve outcomes for this vulnerable population.

Total Steroid Intake is Associated With Hospital Mortality in Patients With Pan-Peritonitis due to Colorectal Perforation

BACKGROUND

Patients with pan-peritonitis (PP) due to colorectal perforation have high mortality rate because colorectal perforation causes septic shock. The association between total steroid intake (TSI) and hospital mortality of such patients is not clear.

METHODS

One hundred forty-two patients who underwent surgery for PP due to colorectal perforation were reviewed. Patients were divided into two groups by 8000 mg of TSI. The cut-off value of TSI was determined using a receiver operating characteristic curve for hospital mortality.

RESULTS

The cut-off value of TSI for hospital mortality was 8000 mg. Patients with TSI>8000 mg had high rate of hemodialysis, hospital mortality, and elevated neutrophil ratio (>95%) compared with those with TSI≤8000 mg. Multivariate analyses revealed that TSI (>8000/≤8000, mg) (OR, 9.669; 95% CI, 1.011-92.49; P = .049) was significantly associated with hospital mortality as well as bleeding volume (>1000/≤1000, mL) (OR, 26.08; 95% CI, 3.566-190.4; P = .001), lymphocyte ratio (≤4/>4, %) (OR, 7.988; 95% CI, 1.498-42.58; P = .015) and C-reactive protein (≤7.5/>7.5, mg/dL) (OR, 41.66; 95% CI, 4.784-33.33; P = .001).

DISCUSSION

There was a significant association between TSI and hospital mortality in patients with PP due to colorectal perforation as well as intraoperative bleeding and systemic inflammatory markers.

LATE POSTPANCREATICODUODENECTOMY HEMORRHAGE: INCIDENCE, RISK FACTORS, MANAGEMENT AND OUTCOME

ABSTRACT

Background:Postpancreaticoduodenectomy (PD) hemorrhage (PPH) is a life-threatening complication after PD. The main objective of this study was to evaluate incidence and factors associated with late PPH as well as the management strategy and outcomes. Methods: Between May 2017 and March 2020, clinical data from 192 patients undergoing PD were collected prospectively in the CHIRPAN Database (NCT02871336) and retrospectively analyzed. In our institution, all patients scheduled for a PD are routinely admitted for monitoring and management in intensive/intermediate care unit (ICU/IMC). Results: The incidence of late PPH was 17% (32 of 192), whereas the 90-day mortality rate of late PPH was 19% (6 of 32). Late PPH was associated with 90-day mortality (P = 0.001). Using multivariate analysis, independent risk factors for late PPH were postoperative sepsis (P = 0.036), and on day 3, creatinine (P = 0.025), drain fluid amylase concentration (P = 0.023), lipase concentration (P < 0.001), and C-reactive protein (CRP) concentration (P < 0.001). We developed two predictive scores for PPH occurrence, the PANCRHEMO scores. Score 1 was associated with 68.8% sensitivity, 85.6% specificity, 48.8% predictive positive value, 93.2% negative predictive value, and an area under the receiver operating characteristic curves of 0.841. Score 2 was associated with 81.2% sensitivity, 76.9% specificity, 41.3% predictive positive value, 95.3% negative predictive value, and an area under the receiver operating characteristic curve of 0.859. Conclusions: Routine ICU/IMC monitoring might contribute to a better management of these complications. Some predicting factors such as postoperative sepsis and biological markers on day 3 should help physicians to determine patients requiring a prolonged ICU/IMC monitoring.

A simple and novel equation to estimate the degree of bleeding in haemorrhagic shock: mathematical derivation and preliminary in vivo validation

Determining blood loss [100% – RBV (%)] is challenging in the management of haemorrhagic shock. We derived an equation estimating RBV (%) via serial haematocrits (Hct₁, Hct₂) by fixing infused crystalloid fluid volume (N) as [0.015 × body weight (g)]. Then, we validated it in vivo. Mathematically, the following estimation equation was derived: RBV (%) = 24k / [(Hct₁ / Hct₂) – 1]. For validation, non-ongoing haemorrhagic shock was induced in Sprague–Dawley rats by withdrawing 20.0%–60.0% of their total blood volume (TBV) in 5.0% intervals (n = 9). Hct₁ was checked after 10 min and normal saline N cc was infused over 10 min. Hct₂ was checked five minutes later. We applied a linear equation to explain RBV (%) with 1 / [(Hct₁ / Hct₂) – 1]. Seven rats losing 30.0%–60.0% of their TBV suffered shock persistently. For them, RBV (%) was updated as 5.67 / [(Hct₁ / Hct₂) – 1] + 32.8 (95% confidence interval [CI] of the slope: 3.14–8.21, p = 0.002, R² = 0.87). On a Bland-Altman plot, the difference between the estimated and actual RBV was 0.00 ± 4.03%; the 95% CIs of the limits of agreements were included within the pre-determined criterion of validation (< 20%). For rats suffering from persistent, non-ongoing haemorrhagic shock, we derived and validated a simple equation estimating RBV (%). This enables the calculation of blood loss via information on serial haematocrits under a fixed N. Clinical validation is required before utilisation for emergency care of haemorrhagic shock.