ObjectiveTo systematically evaluate the risk factors of acute kidney injury after surgery for acute type A aortic dissection.MethodsWe searched the CNKI, Wanfang Database, VIP, PubMed, Web of science, Cochrane Library (from inception to January 2019) to identify studies about the risk factors of acute kidney injury after surgery for acute type A aortic dissection. Quality of the included studies was evaluated by Kars-Ottawa scale. The meta-analysis was performed by RevMan 5.3 software.ResultsA total of 16 case-control studies were included involving 1 728 patients. The results of meta-analysis showed that gender (OR=1.58, 95% CI 1.31 to 1.89, P<0.001), body mass index (OR=1.05, 95% CI 0.66 to 1.45, P<0.001), hypertension (OR=1.58, 95% CI 1.10 to 2.26, P=0.010), smoking history (OR=1.71, 95% CI 1.12 to 2.61, P=0.010), preoperative serum creatinine level (OR=30.26, 95% CI 20.17 to 40.35, P<0.000 01), preoperative white blood cell (OR=1.73, 95% CI 0.26 to 3.20, P=0.020), extracorporeal circulation time (OR=25.60, 95% CI 21.13 to 30.08, P<0.000 01), aortic occlusion time (OR=13.24, 95% CI 10.27 to 16.22, P<0.001), deep hypothermic circulatory arrest (DHCA) time (OR=2.58, 95% CI 0.86 to 4.29, P=0.003), arch replacement (OR=2.31, 95% CI 1.31 to 4.07, P=0.004), intraoperative blood transfusion (OR=1.27, 95% CI 0.29 to 2.24, P=0.010), postoperative mean arterial pressure (OR=–2.41, 95% CI –4.59 to –0.24, P=0.030), reoperation due to postoperative hemorrhage (OR=4.19, 95% CI 2.04 to 8.63, P<0.001), postoperative acute respiratory insufficiency (OR=6.61, 95% CI 3.21 to 13.60, P<0.001), postoperative mechanical ventilation time (OR=48.51, 95% CI 21.94 to 75.09, P<0.001) were associated with acute kidney injury after surgery for acute type A aortic dissection.ConclusionCurrent evidence shows that gender, body mass index, hypertension, smoking history, preoperative serum creatinine level, preoperative white blood cell, extracorporeal circulation time, aortic occlusion time, deep hypothermic circulatory arrest (DHCA) time, arch replacement, intraoperative blood transfusion, postoperative mean arterial pressure, postoperative hemorrhage reoperation, postoperative acute respiratory insufficiency and postoperative mechanical ventilation time were risk factors for acute kidney injury after surgery for type A aortic dissection. Medical staff can strengthen perioperative management of patients with acute type A aortic dissection combined with the above factors, so as to reduce the incidence of acute kidney injury after operation and improve the clinical prognosis of patients.
ObjectivesTo systematically review the delirium risk prediction models in intensive care unit (ICU) patients.MethodsThe Cochrane Library, PubMed, Web of Science, Ovid, VIP, WanFang Date and CNKI databases were electronically searched to collect studies on delirium risk prediction models in intensive care unit patients from inception to December, 2018. Two reviewers independently screened literature, extracted data, evaluated the included studies according to the CHARMS checklist, and then systematic review was performed to evaluate the risk prediction models.ResultsA total of 9 studies were included, of which 7 were prospective studies. Six models were internally validated. All studies reported the area under receiver operating characteristic curve (AUROC) over 0.7 (0.739-0.926). The reduction of cognitive reserve and increased blood urea nitrogen were the most commonly reported predisposing and precipitating factors of delirium among all prediction models. Methodologically, the absence or unreported of the blind method, to a certain extent, partially increase the risk of bias.ConclusionsNine prediction models all have great power in early identifying and screening patients who are at high risk of developing ICU delirium. On the basis of judiciously selecting a practical prediction model for clinical practice or carrying out a large sample-size prospective cohort study to construct the localized prediction model, stratified prevention strategies should be formulated and implemented according to the risk stratification results to reduce the incidence of ICU delirium and accelerate the rational allocation of medical resources for delirium prevention.
ObjectivesTo systematically review the risk factors of postoperative hypoxemia in patients undergoing coronary artery bypass grafting.MethodsPubMed, EBCO, The Cochrane Library, CNKI, VIP and WanFang Data databases were electronically searched to collect case-control studies and cohort studies on the risk factors of postoperative hypoxemia in patients undergoing coronary artery bypass grafting from inception to December 2018. Two reviewers independently screened literature, extracted data and assessed risk of bias of included studies, then, meta-analysis was performed by using RevMan 5.3 software.ResultsA total of 20 articles were included, including 3 926 patients. The results of meta-analysis showed that: age (OR=2.94, 95%CI 0.81 to 5.07, P=0.007), body mass index (OR=1.94, 95%CI 0.77 to 3.12, P=0.001), smoking (OR=2.72, 95%CI 1.68 to 4.42, P<0.000 1), diabetes history (OR=1.63, 95%CI 1.37 to 1.93, P<0.000 01), preoperative lung diseases (OR=4.11, 95%CI 1.64 to 10.28, P=0.003), complicated ventricular aneurysm (OR=1.57, 95%CI 1.12 to 2.21, P=0.01), left ventricular end-diastolic diameter (OR=1.28, 95%CI 0.12 to 2.44, P=0.03), aortic occlusion time (OR=13.25, 95%CI 4.93 to 21.57, P=0.002), operation time (OR=9.33, 95%CI 5.36 to 13.30, P<0.000 01), number of bypass branches (OR=0.19, 95%CI 0.02 to 0.36, P=0.03), intraoperative infusion volume (OR=383.46, 95%CI 282.16 to 484.76, P<0.000 01) and postoperative pulmonary infection (OR=6.00, 95%CI 3.83 to 9.42, P<0.000 01) were the risk factors for postoperative hypoxemia in patients undergoing coronary artery bypass grafting. Preoperative ejection fraction (OR=−2.60, 95%CI −4.56 to −0.64, P=0.009) and preoperative partial oxygen pressure (OR=−3.14, 95%CI −4.72 to −1.56, P=0.000 1) were the protective factors for postoperative hypoxemia.ConclusionsCurrent evidence shows that age, body mass index, smoking, diabetes history, preoperative lung diseases, complicated ventricular aneurysm, left ventricular end-diastolic diameter, aortic occlusion time, operation time, number of bypass branches, intraoperative infusion volume and postoperative pulmonary infection are risk factors for postoperative hypoxemia in patients undergoing coronary artery bypass grafting. Due to limited quality and quantity of included studies, the above conclusion is required to be assessed by further studies.
ObjectivesTo systematically review the risk factors of acute fatigue in patients with stroke.MethodsPubMed, Web of Science, EMbase, The Cochrane Library, CNKI, VIP and WanFang Data databases were electronically searched to collect case-control studies, cohort studies and cross-sectional studies on the risk factors of acute fatigue in patients with stroke from inception to April, 2019. Two reviewers independently screened literature, extracted data and assessed risk of bias of included studies, then, meta-analysis was performed by using RevMan 5.3 software.ResultsA total of 14 studies involving 2 658 objects and 13 risk factors were included. The results of meta-analysis showed that: female (OR=1.54, 95%CI 1.23 to 1.94, P=0.000 2), rural residence (OR=1.46, 95%CI 1.11 to 1.91, P=0.007), diabetes mellitus (OR=1.54, 95%CI 1.24 to 1.92, P<0.000 1), hyperlipidemia (OR=1.41, 95%CI 1.10 to 1.80, P=0.007), coronary heart disease (OR=1.94, 95%CI 1.30 to 2.89, P=0.001), previous stroke history (OR=1.54, 95%CI 1.07 to 2.23, P<0.000 01), pre-stroke fatigue (OR=4.51, 95%CI 3.33 to 6.09, P<0.000 01), basal ganglia stroke (OR=2.76, 95%CI 1.21 to 6.29, P<0.000 01), NIHSS >3 (OR=2.11, 95%CI 1.59 to 2.79, P<0.000 01), admission glucose level (OR=1.08, 95%CI 0.38 to 1.78, P=0.003), post-stroke sleep disorder (OR=2.40, 95%CI 1.87 to 3.07, P<0.000 01), post-stroke pain (OR=2.32, 95%CI 1.56 to 3.45, P<0.000 1) and post-stroke depression (OR=3.31, 95%CI 1.94 to 5.66, P<0.000 1) were risk factors of acute fatigue in patients with stroke.ConclusionsCurrent evidence shows that female, rural residence, diabetes mellitus, hyperlipidemia, coronary heart disease, previous stroke history, pre-stroke fatigue, basal ganglia stroke, NIHSS>3, admission glucose level, post-stroke sleep disorder, post-stroke pain and post-stroke depression are the risk factors of acute fatigue in patients with stroke. Medical staff should strengthen targeted preventive care for high-risk patients with related risk factors in order to reduce the incidence of post-stroke fatigue and improve the clinical prognosis outcome of patients.
ObjectiveTo systematically evaluate the risk factors for perioperative blood transfusion in patients undergoing coronary artery bypass grafting (CABG).MethodsPubMed, Web of Science, The Cochrane Library, EMbase, CNKI, WanFang and VIP Database were electronically searched to collect case-control and cohort studies about the risk factors for perioperative blood transfusion in patients undergoing CABG from inception to February 2020. Two reviewers screened and evaluated the literatures according to the inclusion and exclusion criteria, and meta-analysis was performed by using RevMan 5.3 software.ResultsA total of 26 articles were collected, involving 84 661 patients. The results of meta-analysis showed that age (OR=1.06, 95%CI 1.03 to 1.08, P<0.001), age≥70 years (OR=2.14, 95%CI 1.77 to 2.59, P<0.001), female (OR=1.85, 95%CI 1.55 to 2.22, P<0.001), body mass index (OR=0.94, 95%CI 0.90 to 0.98, P=0.003), weight (OR=0.95, 95%CI 0.93 to 0.96, P<0.001), body surface area (OR=0.19, 95%CI 0.10 to 0.39, P<0.001), smoking (OR=0.80, 95%CI 0.69 to 0.93, P=0.003), diabetes (OR=1.15, 95%CI 1.09 to 1.20, P<0.000 01), chronic heart failure (OR=1.59, 95%CI 1.26 to 1.99, P<0.001), number of diseased coronary arteries (OR=1.17, 95%CI 1.01 to 1.35, P=0.030), reoperation (OR=2.12, 95%CI 1.79 to 2.51, P<0.001), preoperative hemoglobin level (OR=0.60, 95%CI 0.43 to 0.84, P=0.003), preoperative ejection fraction <35% (OR=2.57, 95%CI 1.24 to 5.34, P=0.010), emergency surgery (OR=4.09, 95%CI 2.52 to 6.63, P<0.001), urgent operation (OR=2.28, 95%CI 1.25 to 4.17, P=0.007), intra-aortic balloon pump (OR=3.86, 95%CI 3.35 to 4.44, P<0.001), cardiopulmonary bypass (OR=4.24, 95%CI 2.95 to 6.10, P<0.001), cardiopulmonary bypass time (OR=1.01, 95%CI 1.01 to 1.01, P<0.000 01) and minimum hemoglobin during cardiopulmonary bypass (OR=0.42, 95%CI 0.23 to 0.77, P=0.005) were the risk factors for perioperative blood transfusion in patients undergoing CABG.ConclusionCurrent evidence shows that age, age≥70 years, female, body mass index, weight, body surface area, smoking, diabetes, chronic heart failure, number of diseased coronary arteries, reoperation, preoperative hemoglobin level, preoperative ejection fraction<35%, emergency surgery, urgent operation, intra-aortic balloon pump, cardiopulmonary bypass, cardiopulmonary bypass time and minimum hemoglobin during cardiopulmonary bypass are risk factors for perioperative blood transfusion in patients who undergo CABG. Medical staff should formulate and improve the relevant perioperative blood management measures according to the above risk factors, in order to reduce the perioperative blood utilization rate and improve the clinical prognosis of patients.
ObjectiveTo systematically evaluate the effect of high-flow nasal cannula in immunocompromised patients with acute respiratory failure.MethodsRandomized controlled trials (RCT) or cohort studies on the efficacy of high-flow oxygen therapy in immunocompromised patients with acute respiratory failure were reviewed by computer in PubMed, EMBASE, Cochrane Library, and China Knowledge Network, Wanfang and VIP databases. The group used HFNC and the control group used a mask or a nasal catheter to give oxygen-based conventional oxygen therapy (COT) or noninvasive ventilation (NIV). Two investigators conducted quality assessments and data extractions based on the Cochrane Collaboration Risk Assessment Manual and the Newcastle-Ottawa Scale. Meta analysis was performed using RevMan 5.3 software. The main outcome measures included tracheal intubation rate, and intensive care unit (ICU) mortality. The secondary outcomes included ICU hospitalization time.ResultsThe study included 13 articles (4 RCTs, 9 cohort studies), a total of 1133 subjects, with 583 in the HFNC group and 550 in the control group (280 in the COT and 270 in the NIV). Meta-analysis showed that HFNC was significantly different from COT in reducing tracheal intubation rate in immunocompromised patients with respiratory failure (OR=0.49, 95%CI 0.33 - 0.72, P=0.0003), but no statistical significance compared with NIV (OR=0.73, 95%CI 0.52 - 1.02, P=0.07); two-combination analysis showed that HFNC had a significant advantage in reducing tracheal intubation rate compared with COT/NIV (combined OR=0.61, 95%CI 0.47 - 0.79, P=0.0002). In terms of ICU mortality, there was a statistically significant difference between HFNC and COT (OR=0.59, 95%CI 0.35 - 1.01, P=0.05) or NIV (OR=0.63, 95%CI 0.44 - 0.91, P=0.01). The results of the two subcombinations and analysis did not change (combined OR=0.62, 95%CI 0.46 - 0.83, P=0.002). In terms of ICU hospital stay, there was no statistically significant difference between HFNC and COT (MD=−4.52, 95%CI −9.43 - 0.39, P=0.07), but the difference was statistically significant compared with NIV (MD=−1.46, 95%CI −2.41 - −0.51, P =0.003); the two sub-combinations and analysis results showed significant difference (combined MD=−3.41, 95%CI −6.16 - −0.66, P=0.01). According to different research types, after subgroup analysis, the analysis results were not different from the combined results. Sensitivity analysis revealed that HFNC could significantly reduce the patient's ICU hospital stay compared with the control group oxygen therapy. The results of the funnel chart analysis show that there were publication offsets in the studies on tracheal intubation rate and ICU mortality included in the literature; studies on ICU hospital stays had a smaller publication offset.ConclusionsCompared with COT, HFNC can reduce the tracheal intubation rate of patients, but there is no significant difference compared with NIV; HFNC can reduce the ICU mortality of patients compared with COT/NIV. However, due to the high heterogeneity between the studies, whether HFNC can reduce ICU hospital stay remains to be further explored.