Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set.

During the perioperative period patients often suffer complications, including acute kidney injury (AKI), reintubation, and mortality. In order to effectively prevent these complications, high-risk patients must be readily identified. However, most current risk scores are designed to predict a single postoperative complication and often lack specificity on the patient level. In other fields, machine learning (ML) has been shown to successfully create models to predict multiple end points using a single input feature set. We hypothesized that ML can be used to create models to predict postoperative mortality, AKI, reintubation, and a combined outcome using a single set of features available at the end of surgery. A set of 46 features available at the end of surgery, including drug dosing, blood loss, vital signs, and others were extracted. Additionally, six additional features accounting for total intraoperative hypotension were extracted and trialed for different models. A total of 59,981 surgical procedures met inclusion criteria and the deep neural networks (DNN) were trained on 80% of the data, with 20% reserved for testing. The network performances were then compared to ASA Physical Status. In addition to creating separate models for each outcome, a multitask learning model was trialed that used information on all outcomes to predict the likelihood of each outcome individually. The overall rate of the examined complications in this data set was 0.79% for mortality, 22.3% (of 21,676 patients with creatinine values) for AKI, and 1.1% for reintubation. Overall, there was significant overlap between the various model types for each outcome, with no one modeling technique consistently performing the best. However, the best DNN models did beat the ASA score for all outcomes other than mortality. The highest area under the receiver operating characteristic curve (AUC) models were 0.792 (0.775-0.808) for AKI, 0.879 (0.851-0.905) for reintubation, 0.907 (0.872-0.938) for mortality, and 0.874 (0.864-0.866) for any outcome. The ASA score alone achieved AUCs of 0.652 (0.636-0.669) for AKI, 0.787 (0.757-0.818) for reintubation, 0.839 (0.804-0.875) for mortality, and 0.76 (0.748-0.773) for any outcome. Overall, the DNN architecture was able to create models that outperformed the ASA physical status to predict all outcomes based on a single feature set, consisting of objective data available at the end of surgery. No one model architecture consistently performed the best

Operationalizing Precision Cardiovascular Medicine: Three Innovations.

For precision medicine to become a reality, we propose three changes. First, healthcare deliverables must be prioritized, enabling translation of knowledge to the clinic. Second, physicians and patients must be convinced to participate, requiring additional infrastructure in health systems. Third, discovery science must evolve to shift the preclinical landscape for innovation. We propose a change in the fundamental relationship between basic and clinical science: rather than two distinct entities between which concepts must be translated, we envision a natural hybrid of these approaches, wherein discovery science and clinical trials coincide in the same health systems and patient populations

Validation of a convolutional neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation: an algorithm development experimental study

Background: International guidelines recommend quantitative neuromuscular monitoring when administering neuromuscular blocking agents. The train-of-four count is important for determining the depth of block and appropriate reversal agents and doses. However, identifying valid compound motor action potentials (cMAPs) during surgery can be challenging because of low-amplitude signals and an inability to observe motor responses. A convolutional neural network (CNN) to classify cMAPs as valid or not might improve the accuracy of such determinations. Methods: We modified a high-accuracy CNN originally developed to identify handwritten numbers. For training, we used digitised electromyograph waveforms (TetraGraph) from a previous study of 29 patients and tuned the model parameters using leave-one-out cross-validation. External validation used a dataset of 19 patients from another study with the same neuromuscular block monitor but with different patient, surgical, and protocol characteristics. All patients underwent ulnar nerve stimulation at the wrist and the surface electromyogram was recorded from the adductor pollicis muscle. Results: The tuned CNN performed highly on the validation dataset, with an accuracy of 0.9997 (99% confidence interval 0.9994–0.9999) and F1 score=0.9998. Performance was equally good for classifying the four individual responses in the train-of-four sequence. The calibration plot showed excellent agreement between the predicted probabilities and the actual prevalence of valid cMAPs. Ten-fold cross-validation using all data showed similar high performance. Conclusions: The CNN distinguished valid cMAPs from artifacts after ulnar nerve stimulation at the wrist with >99.5% accuracy. Incorporation of such a process within quantitative electromyographic neuromuscular block monitors is feasible

Perioperative Temperature Measurement Considerations Relevant to Reporting Requirements for National Quality Programs Using Data From Anesthesia Information Management Systems

Perioperative hypothermia may increase the incidences of wound infection, blood loss, transfusion, and cardiac morbidity. US national quality programs for perioperative normothermia specify the presence of at least 1 "body temperature" ≥35.5°C during the interval from 30 minutes before to 15 minutes after the anesthesia end time. Using data from 4 academic hospitals, we evaluated timing and measurement considerations relevant to the current requirements to guide hospitals wishing to report perioperative temperature measures using electronic data sources. Anesthesia information management system databases from 4 hospitals were queried to obtain intraoperative temperatures and intervals to the anesthesia end time from discontinuation of temperature monitoring, end of surgery, and extubation. Inclusion criteria included age >16 years, use of a tracheal tube or supraglottic airway, and case duration ≥60 minutes. The end-of-case temperature was determined as the maximum intraoperative temperature recorded within 30 minutes before the anesthesia end time (ie, the temperature that would be used for reporting purposes). The fractions of cases with intervals >30 minutes between the last intraoperative temperature and the anesthesia end time were determined. Among the hospitals, averages (binned by quarters) of 34.5% to 59.5% of cases had intraoperative temperature monitoring discontinued >30 minutes before the anesthesia end time. Even if temperature measurement had been continued until extubation, averages of 5.9% to 20.8% of cases would have exceeded the allowed 30-minute window. Averages of 8.9% to 21.3% of cases had end-of-case intraoperative temperatures <35.5°C (ie, a quality measure failure). Because of timing considerations, a substantial fraction of cases would have been ineligible to use the end-of-case intraoperative temperature for national quality program reporting. Thus, retrieval of postanesthesia care unit temperatures would have been necessary. A substantive percentage of cases had end-of-case intraoperative temperatures below the 35.5°C threshold, also requiring postoperative measurement to determine whether the quality measure was satisfied. Institutions considering reporting national quality measures for perioperative normothermia should consider the technical and logistical issues identified to achieve a high level of compliance based on the specified regulatory language

Postoperative Acute Kidney Injury is Associated with Persistent Renal Dysfunction: A Multicenter Propensity Matched Cohort Study

BackgroundThe risk of developing a persistent reduction in renal function after postoperative acute kidney injury (pAKI) is not well-established.ObjectivePerform a multi-center retrospective propensity matched study evaluating whether patients that develop pAKI have a greater decline in long-term renal function than patients that did not develop postoperative AKI.DesignMulti-center retrospective propensity matched study.SettingAnesthesia data warehouses at three tertiary care hospitals were queried.PatientsAdult patients undergoing surgery with available preoperative and postoperative creatinine results and without baseline hemodialysis requirements.MeasurementsThe primary outcome was a decline in follow-up glomerular filtration rate (GFR) of 40% relative to baseline, based on follow-up outpatient visits from 0-36 months after hospital discharge. A propensity score matched sample was used in Kaplan-Meier analysis and in a piecewise Cox model to compare time to first 40% decline in GFR for patients with and without pAKI.ResultsA total of 95,208 patients were included. The rate of pAKI ranged from 9.9% to 13.7%. In the piecewise Cox model, pAKI significantly increased the hazard of a 40% decline in GFR. The common effect hazard ratio was 13.35 (95% CI: 10.79 to 16.51, p<0.001) for 0-6 months, 7.07 (5.52 to 9.05, p<0.001) for 6-12 months, 6.02 (4.69 to 7.74, p<0.001) for 12-24 months, and 4.32 (2.65 to 7.05, p<0.001) for 24-36 months.LimitationsRetrospective; Patients undergoing ambulatory surgery without postoperative lab tests drawn before discharge were not captured; certain variables like postoperative urine output were not reliably available.ConclusionPostoperative AKI significantly increases the risk of a 40% decline in GFR up to 36 months after the index surgery across three institutions

External validation of a predictive model for reintubation after cardiac surgery: A retrospective, observational study

Explore validation of a model to predict patients' risk of failing extubation, to help providers make informed, data-driven decisions regarding the optimal timing of extubation. We performed temporal, geographic, and domain validations of a model for the risk of reintubation after cardiac surgery by assessing its performance on data sets from three academic medical centers, with temporal validation using data from the institution where the model was developed. Three academic medical centers in the United States. Adult patients arriving in the cardiac intensive care unit with an endotracheal tube in place after cardiac surgery. Receiver operating characteristic (ROC) curves and concordance statistics were used as measures of discriminative ability, and calibration curves and Brier scores were used to assess the model's predictive ability. Temporal validation was performed in 1642 patients with a reintubation rate of 4.8%, with the model demonstrating strong discrimination (optimism-corrected c-statistic 0.77) and low predictive error (Brier score 0.044) but poor model precision and recall (Optimal F1 score 0.29). Combined domain and geographic validation were performed in 2041 patients with a reintubation rate of 1.5%. The model displayed solid discriminative ability (optimism-corrected c-statistic = 0.73) and low predictive error (Brier score = 0.0149) but low precision and recall (Optimal F1 score = 0.13). Geographic validation was performed in 2489 patients with a reintubation rate of 1.6%, with the model displaying good discrimination (optimism-corrected c-statistic = 0.71) and predictive error (Brier score = 0.0152) but poor precision and recall (Optimal F1 score = 0.13). The reintubation model displayed strong discriminative ability and low predictive error within each validation cohort. Future work is needed to explore how to optimize models before local implementation. •Predictive model validation is frequently proposed but rarely done in anesthesiology.•Multicenter validation has regulatory and logistical barriers.•A federated learning approach, as we demonstrate, helps to overcome these barriers

Impact of Enhanced Recovery After Surgery and Opioid-Free Anesthesia on Opioid Prescriptions at Discharge From the Hospital

BackgroundThe United States is in the midst of an opioid epidemic, and opioid use disorder often begins with a prescription for acute pain. The perioperative period represents an important opportunity to prevent chronic opioid use, and recently there has been a paradigm shift toward implementation of enhanced recovery after surgery (ERAS) protocols that promote opioid-free and multimodal analgesia. The objective of this study was to assess the impact of an ERAS intervention for colorectal surgery on discharge opioid prescribing practices.MethodsWe conducted a historical-prospective quality improvement study of an ERAS protocol implemented for patients undergoing colorectal surgery with a focus on the opioid-free and multimodal analgesia components of the pathway. We compared patients undergoing colorectal surgery 1 year before implementation (June 15, 2015, to June 14, 2016) and 1 year after implementation (June 15, 2016, to June 14, 2017).ResultsBefore the ERAS intervention, opioids at discharge were not significantly increasing (1% per month; 95% confidence interval [CI], -1% to 3%; P = .199). Immediately after the ERAS intervention, opioid prescriptions were not significantly lower (13%; 95% CI, -30% to 3%; P = .110). After the intervention, the rate of opioid prescriptions at discharge did not decrease significantly 1% (95% CI, -3% to 1%) compared to the pre-period rate (P = .399). Subgroup analysis showed that in patients with a combination of low discharge pain scores, no preoperative opioid use, and low morphine milligram equivalents consumption before discharge, the rate of discharge opioid prescription was 72% (95% CI, 61%-83%).ConclusionsThis study is the first to report discharge opioid prescribing practices in an ERAS setting. Although an ERAS intervention for colorectal surgery led to an increase in opioid-free anesthesia and multimodal analgesia, we did not observe an impact on discharge opioid prescribing practices. The majority of patients were discharged with an opioid prescription, including those with a combination of low discharge pain scores, no preoperative opioid use, and low morphine milligram equivalents consumption before discharge. This observation in the setting of an ERAS pathway that promotes multimodal analgesia suggests that our findings are very likely to also be observed in non-ERAS settings and offers an opportunity to modify opioid prescribing practices on discharge after surgery. For opioid-free anesthesia and multimodal analgesia to influence the opioid epidemic, the dose and quantity of the opioids prescribed should be modified based on the information gathered by in-hospital pain scores and opioid use as well as pain history before admission