The Nara Medical University Institutional Review Board approved access to the data stored on the RRa device pulse oximeter and the review of clinical charts of postoperative patients with continuously monitored RRa (Chairperson Prof. N Kurumadani, Nara Medical University, Kashihara, Nara, Japan, 634–8522, Japan), approval No. 1057 on 08–21-2015. The requirement for written informed consents for this historical study was waived.
Standard perioperative patient treatment
We excluded cases requiring intensive care or similar treatment from RRa monitoring because mechanical ventilation or manual RR monitoring, such as intermittent auscultation every 1–2 h, was required in these cases. We applied RRa monitoring to extubated surgical patients outside of intensive care units. The application was limited to elective cases, partially due to a shortage of RRa devices. We considered patients who underwent craniotomy, thyroidectomy, spinal surgery, laparotomy, laparoscopic surgery, or major orthopedic surgery as candidates for RRa monitoring.
The methods of anesthetic induction and maintenance, as well as those for tracheal intubation, were not standardized for each patient. We used sevoflurane or propofol to maintain anesthesia and fentanyl or remifentanil for analgesia. Rocuronium was used for neuromuscular blockade and sugammadex for the reversal of the neuromuscular blockade, after evaluation of the status of the neuromuscular blockade using a nerve stimulator. The attending anesthetist managed the fluids at their discretion and performed transfusions if necessary. As mentioned, we provided postoperative analgesia with IV-fentanyl (0.2–0.8 μg/kg/h) using a commercially available patient-controlled analgesia (PCA) device (Coopdech Syrinjector PCA Device™, Daiken Medical, Osaka City, Japan). The PCA bolus sizes and lockout timings were set at 0.2–0.8 μg/kg and 10 min, respectively. Low-dose droperidol (1.25–2.5 mg/day) was combined with PCA administration. In the immediate postoperative period (1–2 h postoperatively), caregivers administered each PCA bolus, depending on the individual patient situation. Each time a PCA bolus was given, it was recorded in the medical chart. Upon discharge from the operating room, an adhesive acoustic respiration sensor (RAS-125™ or RAS-125™ rev C, Masimo) and an oximetry sensor (LNCS Adtx, Masimo) were placed on the patient’s neck and finger, respectively, and were connected to an RRa device pulse oximeter to monitor and regularly record the RR, SpO2, and pulse rate (PR).
The acoustic sensor was placed on either side of the larynx, above the thyroid cartilage, and below the jaw line according to the manufacturer’s instructions. Oxygen (3–5 l/min) was administered through an oxygen mask, according to our institutional standard practice. Patients were then directly transferred from the operating room to a general surgical ward. The general hospital setting had a nurse-to-patient ratio of 1:7 and comprised a surgical population undergoing miscellaneous surgeries with opioid-based PCA. The nurses at our institute were trained to deliver standard care and to pat a patient on the shoulder or chest while calling or orally encouraging them to breathe deeply in the case of an RRa or SpO2 alarm. However, the nurses seldom adjusted the oxygen delivery rate. Patients were continuously monitored with an RRa device containing a pulse oximeter as per the standard of care, and the monitored data were stored in the internal memory. We set RR, SpO2, and PR alarms at RR < 8 or > 30 breaths per min, < 90% SpO2 with supplemental oxygen, or PR > 130 or < 50 beats per min, respectively, according to the institutional protocol. The RRs and SpO2 in the Rad-87 were calculated by each 10-s interval moving average every 2 s and by each 8-s interval moving average every 1 s, respectively. The RRa device pulse oximetry data were temporarily and automatically stored in an internal memory for up to 72 h with a 2-s resolution. Every time that Rad-87 monitoring was terminated, clinical engineers transferred the data to a storage device using downloaded software (TrendCom ver. 3460, Masimo) and saved the file at our institute.
Data handling and statistical analysis
We collected data from May 1, 2012, to October 31, 2013. During this period, 1253 adult surgical patients were monitored with an RRa device pulse oximeter. We extracted the cumulatively stored data and used the data collected during the first postoperative hour for analyses. We used sophisticated artifact detection algorithms to identify invalid artifact periods in the stored data. That way we confirmed data reliability after prescreening the raw data. We excluded (1) cases with missing datasets or signal loss during the recording process (n = 267); (2) cases undergoing any procedures other than laparotomy, laparoscopic, or major orthopedic surgeries (n = 463) (because generally IV-fentanyl was not provided to these cases according to the institutional protocol); and (3) cases with postoperative epidural analgesia or without IV-fentanyl use (n = 265). In the end, we used the data from 258 patients for this study (Fig. 1).
In addition, we extracted perioperative data from the patients’ clinical charts. On the basis of the analyzed data, we defined an oxygen desaturation event as that with an SpO2 < 90% for > 10 s and bradypnea as an RR < 8 breaths per min for > 2 min, based on arbitrary local rules [12]. Next, we calculated the fentanyl effect-site concentrations at the end of surgery and 1 h postoperatively using BeConSim (owned and produced by Kenichi Masui, Anesthesiology, National Defense Medical College, http://www.masuinet.com/), based on the intraoperative administration profiles of fentanyl and usages of IV-fentanyl including PCA counts.
Subsequently, we divided the patients into the following two groups: (1) the bradypnea group and (2) the normal RR group. Initially, we used univariate logistic regression analyses to identify candidate factors associated with bradypnea. Variables such as age, gender, height, body weight, body mass index (BMI), % vital capacity (%VC), forced expiratory volume % in 1 s, American Society of Anesthesiologists’ physical status classification, sleep apnea syndrome history, renal dysfunction, liver dysfunction, hypertension, asthma, chronic obstructive pulmonary disease, hyperlipidemia, diabetes mellitus, ischemic heart disease, chronic heart failure, smoking, hemodialysis, anesthesia method, surgical site, surgery and anesthesia duration, fluid balance, transfusion balance, total fluid balance (fluid balance + transfusion balance), IV-fentanyl infusion rate, PCA usage count, and effect-site concentrations of fentanyl at the end of surgery and 1 h postoperatively are presented as adjusted odds ratios (ORs) for the development of bradypnea with 95% confidence intervals (CIs). Regarding each medical history, only the presence (or absence) of illness was considered but not the disease severity. In short, the presence or absence of disease was based upon the results of preoperative abnormal tests, or history-based documents. We used explanatory factors exhibiting a significant univariate association (p < 0.20) with bradypnea to construct a forced-entry multivariate logistic regression model and presented them as adjusted ORs with 95% CIs. Interactions between the variables were systematically searched, and collinearity was considered when r or rho was > 0.8, according to the Pearson or Spearman’s coefficient matrix correlation. However, we forced the calculated fentanyl effect-site concentrations into the final model regardless of their statistical significance in the univariate analysis based on our hypothesis. We assessed the discrimination of the final model for bradypnea based on the likelihood ratio test. We used the Hosmer–Lemeshow statistical method to calibrate our model. The area under the receiver operating characteristic (ROC) curve was computed as a descriptive tool for measuring model biases. In addition, we compared the incidence of oxygen desaturation and delivered oxygen flow between the groups. Data are expressed as mean (standard deviation) for normally distributed variables and as median (interquartile range) for non-Gaussian distributed ones. The comparison of two means was performed using Student’s t test and that of two medians or two proportions using the Mann–Whitney U test and the χ2 test or Fisher’s exact method, respectively. We used the SPSS statistical package (SPSS for Windows, Version 24.0. Chicago, SPSS) to perform all the analyses.
