Performance Comparison of Pulmonary Risk Scoring Systems in Lung Resection.

OBJECTIVE: To validate and compare the performance of different pulmonary risk scoring systems to predict postoperative pulmonary complications (PPCs) in lung resection surgery. DESIGN: Retrospective cohort study SETTING: A historic single-center cohort of lung resection surgeries PARTICIPANTS: Adult patients undergoing lung resection surgery under 1-lung ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The accuracy of the following pulmonary risk scoring systems were used to predict pulmonary complications: the ARISCAT (Assess respiratory RIsk in Surgical patients in CATalonia), the LAS VEGAS (Local Assessment of VEntilatory management during General Anesthesia for Surgery), the SPORC (Score for Prediction of Postoperative Respiratory Complications), and a recent thoracic-specific risk score, named CARDOT. Discrimination and calibration were assessed using the concordance (c) index and the intercept of LOESS (locally estimated scatterplot)-smoothed curves, respectively. Additional models were constructed that incorporated predicted postoperative forced expiratory volume (ppoFEV1) into each scoring system. Of the 2,104 patients undergoing lung surgery, 123 developed postoperative pulmonary complications (PPCs; 5.9%). All scoring systems had poor discriminatory power to predict PPCs (ARISCAT c-index 0.60, 95% confidence interval [CI] 0.55-0.65; LAS VEGAS c-index 0.68, 95% CI 0.63-0.73; SPORC c-index 0.63, 95% CI 0.59-0.68; CARDOT c-index 0.64, 95% CI 0.58-0.70), but the inclusion of ppoFEV1 slightly improved the performance of LAS VEGAS (c-index 0.70, 95% CI 0.66-0.75) and CARDOT (c-index 0.68, 95% CI 0.62-0.73). Analysis of calibration showed a slight overestimation when using ARISCAT (intercept -0.28) and LAS VEGAS (intercept -0.27). CONCLUSIONS: None of the scoring systems appeared to have adequate discriminatory power to predict PPCs among patients undergoing lung resection. An alternative risk score is necessary to better predict patients at risk of PPCs after thoracic surgery.

Personalized prescription of ACEI/ARBs for hypertensive COVID-19 patients.

The COVID-19 pandemic has prompted an international effort to develop and repurpose medications and procedures to effectively combat the disease. Several groups have focused on the potential treatment utility of angiotensin-converting-enzyme inhibitors (ACEIs) and angiotensin-receptor blockers (ARBs) for hypertensive COVID-19 patients, with inconclusive evidence thus far. We couple electronic medical record (EMR) and registry data of 3,643 patients from Spain, Italy, Germany, Ecuador, and the US with a machine learning framework to personalize the prescription of ACEIs and ARBs to hypertensive COVID-19 patients. Our approach leverages clinical and demographic information to identify hospitalized individuals whose probability of mortality or morbidity can decrease by prescribing this class of drugs. In particular, the algorithm proposes increasing ACEI/ARBs prescriptions for patients with cardiovascular disease and decreasing prescriptions for those with low oxygen saturation at admission. We show that personalized recommendations can improve patient outcomes by 1.0% compared to the standard of care when applied to external populations. We develop an interactive interface for our algorithm, providing physicians with an actionable tool to easily assess treatment alternatives and inform clinical decisions. This work offers the first personalized recommendation system to accurately evaluate the efficacy and risks of prescribing ACEIs and ARBs to hypertensive COVID-19 patients.

Development and implementation of a dedicated postoperative evaluation service to improve compliance of postoperative visits.

BACKGROUND AND AIMS: Postoperative patient evaluation is an integral component of perioperative care. An audit of our anesthesia department's records demonstrated a compliance rate of <50%. We postulated that the development of clinical anesthesia service dedicated to performing such evaluations would improve compliance significantly. MATERIALS AND METHODS: This retrospective study examined postoperative follow-up completion rate at a large academic center. Data were collected from 58,000 anesthetics during three periods, between each of which an intervention was introduced. The first period examined completion rate when postoperative evaluations were left to the team performing the anesthetic. During the second period, this task was delegated to groups of anesthesiologists based on surgical subspecialty; these smaller groups utilized rotating residents. The third period examined completion rate after implementation of a postoperative evaluation service. All periods utilized the department's electronics database to identify operative patients. The number of adverse anesthesia events reported was also recorded. RESULTS: A significant difference in the proportions of compliance with postoperative evaluations among all three periods was detected. Compliance was 47% during period one and improved to 66% during period two. During period three, which employed a postoperative evaluation service, compliance was 83%. The number of adverse events reported per month increased significantly following the first study period from 95 reported monthly events to 215 and 201 in the second and third periods, respectively. CONCLUSION: By creating a dedicated postoperative evaluation service using available technology, we improved postoperative evaluation completion rate from 47% to 83%, and demonstrated a significant increase in the number of adverse events reported. Based on this, we support the deployment of a dedicated service as a quality improvement initiative.

Postoperative and postdischarge nausea and vomiting following ambulatory eye, head, and neck surgeries: a retrospective cohort study comparing incidence and associated factors.

BACKGROUND: Ambulatory surgery is often followed by the development of nausea and/or vomiting (N/V). Although risk factors for postoperative nausea and vomiting (PONV) are frequently discussed, the distinction between PONV and postdischarge nausea and vomiting (PDNV) is unclear. This is especially troublesome given the potential consequences of postdischarge nausea and vomiting (PDNV), which include major discomfort and hospital readmission. METHODS: In this retrospective cohort study, data from 10,231 adult patients undergoing ambulatory ophthalmology or otolaryngology procedures with general anesthesia were collected and analyzed. Binary and multinomial logistic regression was used to assess the association between patient and anesthetic characteristics (including age, body mass index (BMI), American Society of Anesthesiologists Physical Status (ASA P/S) classification, current smoker status, and intra- and postoperative opioid usage) and the odds ratios of experiencing only PDNV, only PONV, or both PONV and PDNV, as compared to not experiencing N/V at all. RESULTS: We found that 17.8% of all patients developed N/V (PONV and/or PDNV). Patients who experienced PONV had a 2.79 (95% confidence interval 2.24-3.46) times greater risk of reporting PDNV. Binary logistic regression found that younger age, opioid use, and female sex were associated with an increased likelihood of experiencing any N/V. Increased use of nitrous oxide and a higher ASA P/S class was associated with elevated likelihood of PONV, but not PDNV or PONV plus PDNV. CONCLUSIONS: Patients experiencing N/V in the PACU are observed to develop PDNV disproportionately by a factor of 2.79. The patients have distinct predictors, indicating important opportunities for care improvements beyond current guidelines.

Postoperative Hypoxemia After Dual-Controlled vs Volume-Controlled Ventilation in Lung Surgery.

BACKGROUND: One-lung ventilation for thoracic surgery represents a challenge due to the risk for hypoxemia and barotrauma. Dual-controlled ventilation (ie, pressure-regulated volume control [PRVC]) may confer improved lung mechanics compared with conventional ventilation (volume-controlled ventilation [VCV]). Our objective was to determine the association between ventilatory mode and pulmonary outcomes after lung resection surgery. METHODS: A historical cohort (2016-2021) of patients undergoing lung resection surgery was used to identify cases performed with PRVC ventilation (intervention) vs VCV (conventional). Both groups were matched in a 1:1 fashion using propensity scoring based on preoperative oxygen saturation, chronic obstructive pulmonary disease, intraoperative ventilator settings, and surgical approach. Our primary outcome was postoperative hypoxemia (oxygen saturation <92% requiring supplemental oxygen longer than 2 hours). Secondary outcomes included respiratory failure, pneumonia, atelectasis, acute respiratory distress syndrome, pleural effusion, and reintubation. Associations were reported using adjusted odds ratios (aOR). RESULTS: Of 2107 eligible patients (PRVC = 1587 vs VCV = 520), a total of 774 matched pairs were analyzed (PRVC = 387 vs VCV = 387). The overall incidence of postoperative hypoxemia was 35.5% (95% CI 32.2%-39.0%). Hypoxemia was less likely among patients managed with low tidal volumes (≤6 mL/kg per ideal body weight, aOR 0.73, 95% CI 0.57-0.92, P = .008). No significant association was observed between ventilator mode and postoperative hypoxemia (33.3% in PRVC vs 37.7% in VCV; aOR 0.93, 95% CI 0.71-1.23, P = .627) or secondary pulmonary complications (3.9% in PRVC vs 3.4% in VCV; aOR 0.96, 95% CI 0.47-1.97, P = .909). CONCLUSIONS: Dual-controlled ventilation was not associated with improved pulmonary outcomes compared with conventional ventilation in lung resection surgery.

Evaluating disparities in code status designation among patients admitted with COVID-19 at a quaternary care center early in the pandemic.

The COVID-19 pandemic has highlighted disparities in outcomes by social determinants to health. It is unclear how much end-of-life discussions and a patient's decision about code status ("do not resuscitate," do not resuscitate, or "comfort measures only," [CMO] orders) might contribute to in hospital disparities in care, especially given know racial inequities in end-of-life care. Here, we looked at factors associated with code status orders at the end of hospitalization for patients with COVID-19. We conducted a retrospective chart review of all patients who presented to the Emergency Department of a large quaternary hospital between 8 March and 3 June 2020. We used logistic regression modeling to quantify the degree to which social determinants of health, including race, ethnicity, area deprivation index (ADI), English as a primary language, homelessness, and illicit substance use might impact the likelihood of a particular code status at the end-of a patient's hospitalization, while controlling for disease severity. Among social determinants to health, only white race (odds ratio [OR] 2.0; P = .03) and higher ADI (OR 1.2; P = .03) were associated with having a do not resuscitate or a CMO order. Additionally, we found that patients with white race (OR 2.9; P = .02) were more likely to carry a CMO order. Patient race and ADI were associated with different code status orders at the end of hospitalization. Differences in code status might have contributed to disparities in COVID-19 outcomes early in the pandemic, though further investigations are warranted.

Risk assessment for acute kidney injury and death among new COVID-19 positive adult patients without chronic kidney disease: retrospective cohort study among three US hospitals.

OBJECTIVE: To develop simple but clinically informative risk stratification tools using a few top demographic factors and biomarkers at COVID-19 diagnosis to predict acute kidney injury (AKI) and death. DESIGN: Retrospective cohort analysis, follow-up from 1 February through 28 May 2020. SETTING: 3 teaching hospitals, 2 urban and 1 community-based in the Boston area. PARTICIPANTS: Eligible patients were at least 18 years old, tested COVID-19 positive from 1 February through 28 May 2020, and had at least two serum creatinine measurements within 30 days of a new COVID-19 diagnosis. Exclusion criteria were having chronic kidney disease or having a previous AKI within 3 months of a new COVID-19 diagnosis. MAIN OUTCOMES AND MEASURES: Time from new COVID-19 diagnosis until AKI event, time until death event. RESULTS: Among 3716 patients, there were 1855 (49.9%) males and the average age was 58.6 years (SD 19.2 years). Age, sex, white blood cell, haemoglobin, platelet, C reactive protein (CRP) and D-dimer levels were most strongly associated with AKI and/or death. We created risk scores using these variables predicting AKI within 3 days and death within 30 days of a new COVID-19 diagnosis. Area under the curve (AUC) for predicting AKI within 3 days was 0.785 (95% CI 0.758 to 0.813) and AUC for death within 30 days was 0.861 (95% CI 0.843 to 0.878). Haemoglobin was the most predictive component for AKI, and age the most predictive for death. Predictive accuracies using all study variables were similar to using the simplified scores. CONCLUSION: Simple risk scores using age, sex, a complete blood cell count, CRP and D-dimer were highly predictive of AKI and death and can help simplify and better inform clinical decision making.

Identifying Patients Vulnerable to Inadequate Pain Resolution After Cardiac Surgery.

Acute postoperative pain (APOP) is often evaluated through granular parameters, though monitoring postoperative pain using trends may better describe pain state. We investigated acute postoperative pain trajectories in cardiac surgical patients to identify subpopulations of pain resolution and elucidate predictors of problematic pain courses. We examined retrospective data from 2810 cardiac surgical patients at a single center. The k-means algorithm for longitudinal data was used to generate clusters of pain trajectories over the first 5 postoperative days. Patient characteristics were examined for association with cluster membership using ordinal and multinomial logistic regression. We identified 3 subgroups of pain resolution after cardiac surgery: 37.7% with good resolution, 44.2% with moderate resolution, and 18.2% exhibiting poor resolution. Type I diabetes (2.04 [1.00-4.16], p = 0.05), preoperative opioid use (1.65 [1.23-2.22], p = 0.001), and illicit drug use (1.89 [1.26-2.83], p = 0.002) elevated risk of membership into worse pain trajectory clusters. Female gender (1.72 [1.30-2.27], p < 0.001), depression (1.60 [1.03-2.50], p = 0.04) and chronic pain (3.28 [1.79-5.99], p < 0.001) increased risk of membership in the worst pain resolution cluster. This study defined 3 APOP resolution subgroups based on pain score trend after cardiac surgery and identified factors that predisposed patients to worse resolution. Patients with moderate or poor pain trajectory consumed more opioids and received them for longer before discharge. Future studies are warranted to determine if altering postoperative pain monitoring and management improve postoperative course of patients at risk of moderate or poor pain resolution.

Reducing Medication Waste While Improving Access to Sugammadex: A Quality Improvement Project in Medication Stewardship.

The relatively high cost of sugammadex compared to neostigmine limits its widespread use to reverse neuromuscular blockade, despite its faster onset and more complete clinical effect. While ensuring timely access to sugammadex is important in improving perioperative safety, it is also vital to control unnecessary spending. We describe a quality improvement initiative to reduce excess spending on sugammadex while improving access for anesthesia providers. Monthly spending on sugammadex decreased by 52% ($70,777 vs $33,821), while medication access increased via automated medication dispensers in each operating room. Clinical usage decreased by one-third, with presumed increased adherence to dosing guidelines.

An improved model for predicting postoperative nausea and vomiting in ambulatory surgery patients using physician-modifiable risk factors.

OBJECTIVE: Postoperative nausea and vomiting (PONV) is a frequent complication in patients undergoing ambulatory surgery, with an incidence of 20%-65%. A predictive model can be utilized for decision support and feedback for practitioner practice improvement. The goal of this study was to develop a better model to predict the patient's risk for PONV by incorporating both non-modifiable patient characteristics and modifiable practitioner-specific anesthetic practices. MATERIALS AND METHODS: Data on 2505 ambulatory surgery cases were prospectively collected at an academic center. Sixteen patient-related, surgical, and anesthetic predictors were used to develop a logistic regression model. The experimental model (EM) was compared against the original Apfel model (OAM), refitted Apfel model (RAM), simplified Apfel risk score (SARS), and refitted Sinclair model (RSM) by examining the discriminating power calculated using area under the curve (AUC) and by examining calibration curves. RESULTS: The EM contained 11 input variables. The AUC was 0.738 for the EM, 0.620 for the OAM, 0.629 for the RAM, 0.626 for the SARS, and 0.711 for the RSM. Pair-wise discrimination comparison of models showed statistically significant differences (p<0.05) in AUC between the EM and all other models, OAM and RSM, RAM and RSM, and SARS and RSM. DISCUSSION: All models except the OAM appeared to have good calibration for our institution's ambulatory surgery data. Ours is the first model to break down risk by anesthetic technique and incorporate risk reduction due to PONV prophylaxis. CONCLUSION: The EM showed statistically significant improved discrimination over existing models and good calibration. However, the EM should be validated at another institution.

Presence of anesthesia resident trainees in day surgery unit has mixed effects on operating room efficiency measures.

BACKGROUND: Three competing goals at academic medical centers are to increase efficiency, to optimize clinical care, and to train residents. The goal of this project was to compare day surgery operating room (OR) efficiency measures for anesthesiologists working alone, working with residents, and working with certified nurse anesthetists in a tertiary multisubspecialty teaching hospital to determine if trainees significantly impact OR efficiency. METHODS: We retrospectively evaluated operating room times data for 2,427 day surgery cases, comparing first case on-time starts, anesthesia-controlled times, induction times, emergence times, and turnover times for the 3 anesthesiologist groups. RESULTS: Compared to the solo anesthesiologist group, anesthesiologists working with residents had significantly longer induction, emergence, and total anesthesia-controlled times (20.2 ± 8.0 vs 18.4 ± 7.0 minutes). However, the anesthesiologists working with residents had more on-time starts (65% vs 53%) and lower turnover times 47.3 ± 13.6 vs 50.8 ± 14.5 minutes) than the solo anesthesiologist group. CONCLUSION: The pairing of anesthesiology residents with anesthesia staff has mixed effects on OR efficiency measures in a day surgery unit.

Specialized ambulatory anesthesia teams contribute to decreased ambulatory surgery recovery room length of stay.

BACKGROUND: Many institutions have organized specialized groups of ambulatory surgery anesthesiologists with the aim of improving ambulatory surgery patient care and efficiency. We hypothesized that specialized ambulatory anesthesia teams produce better patient outcomes such as lower postoperative nausea and vomiting (PONV) rates, lower postoperative pain scores, and shorter postanesthesia care unit (PACU) lengths of stay (LOS). METHODS: In this prospective observational study, we collected outcomes data on 1,299 patients including incidence of PONV, PACU LOS, maximum and average pain scores, amount of postoperative opioid use, and rescue antiemetic use. RESULTS: Ambulatory anesthesiologists had statistically shorter phase 2 PACU LOS times (P < .05) and overall recovery times (P < .01). The PONV incidence odds ratio for ambulatory versus nonambulatory anesthesiologists was 1.31 (95% CI 1.01-1.72). We found no significant difference in the amount of postoperative opioid use, maximum postoperative pain scores, or PACU phase 1 LOS time. CONCLUSIONS: The decreased PACU LOS for the study group's patients occurred despite the increased incidence of PONV. Ambulatory anesthesiologists contributed to decreased PACU LOS while practicing evidence-based anesthesia with regard to PONV and pain control. Ambulatory subspecialization may benefit institutions as a way to increase perioperative efficiency and improve surgeon and patient satisfaction.

Encouraging change in anesthesiology practice through electronic feedback to physicians: results from prototype system.

Implementation of best care practices is difficult because the status quo is often perpetuated; many providers treat patients based on anecdotal experience rather than evidence-based medicine. Our goal was to develop and evaluate an electronic feedback system that feeds back practice and outcome data combined with educational material to anesthesiologists. Best care practices for postoperative nausea/vomiting (PONV) control were selected to evaluate this system because PONV is a common outcome and guidelines have been published.

Anesthesia Online Research & Training Aid (AORTA): developing an automated online querying tool for clinicians.

The Anesthesia Online Research & Training Aid (AORTA) has ben developed to automatically query multiple online resources using terminology with which clinicians ae already familiar and to prevent filtered results in an easy to use manner. AORTA aims to provide clinicians with higher quality results that are useful for daily care of patients and for busy clinicians to keep up to date on practices.