Mayumi Horibe

Anesthesia information management system-based near real-time decision support to manage intraoperative hypotension and hypertension

BACKGROUND:Intraoperative hypotension and hypertension are associated with adverse clinical outcomes and morbidity. Clinical decision support mediated through an anesthesia information management system (AIMS) has been shown to improve quality of care. We hypothesized that an AIMS-based clinical decision support system could be used to improve management of intraoperative hypotension and hypertension. METHODS:A near real-time AIMS-based decision support module, Smart Anesthesia Manager (SAM), was used to detect selected scenarios contributing to hypotension and hypertension. Specifically, hypotension (systolic blood pressure <80 mm Hg) with a concurrent high concentration (>1.25 minimum alveolar concentration [MAC]) of inhaled drug and hypertension (systolic blood pressure >160 mm Hg) with concurrent phenylephrine infusion were detected, and anesthesia providers were notified via “pop-up” computer screen messages. AIMS data were retrospectively analyzed to evaluate the effect of SAM notification messages on hypotensive and hypertensive episodes. RESULTS:For anesthetic cases 12 months before (N = 16913) and after (N = 17132) institution of SAM messages, the median duration of hypotensive episodes with concurrent high MAC decreased with notifications (Mann Whitney rank sum test, P = 0.031). However, the reduction in the median duration of hypertensive episodes with concurrent phenylephrine infusion was not significant (P = 0.47). The frequency of prolonged episodes that lasted >6 minutes (sampling period of SAM), represented in terms of the number of cases with episodes per 100 surgical cases (or percentage occurrence), declined with notifications for both hypotension with >1.25 MAC inhaled drug episodes (&dgr; = −0.26% [confidence interval, −0.38% to −0.11%], P < 0.001) and hypertension with phenylephrine infusion episodes (&dgr; = −0.92% [confidence interval, −1.79% to −0.04%], P = 0.035). For hypotensive events, the anesthesia providers reduced the inhaled drug concentrations to <1.25 MAC 81% of the time with notifications compared with 59% without notifications (P = 0.003). For hypertensive episodes, although the anesthesia providers’ reduction or discontinuation of the phenylephrine infusion increased from 22% to 37% (P = 0.030) with notification messages, the overall response was less consistent than the response to hypotensive episodes. CONCLUSIONS:With automatic acquisition of arterial blood pressure and inhaled drug concentration variables in an AIMS, near real-time notification was effective in reducing the duration and frequency of hypotension with concurrent >1.25 MAC inhaled drug episodes. However, since phenylephrine infusion is manually documented in an AIMS, the impact of notification messages was less pronounced in reducing episodes of hypertension with concurrent phenylephrine infusion. Automated data capture and a higher frequency of data acquisition in an AIMS can improve the effectiveness of an intraoperative clinical decision support system.

A novel computerized fading memory algorithm for glycemic control in postoperative surgical patients.

BACKGROUND: Hyperglycemia is commonly encountered in critically ill patients and is associated with increased mortality and morbidity. To better control blood glucose levels, we previously developed a new computerized fading memory (FM) algorithm.1 In this study we evaluated the safety and efficacy of this algorithm in surgical intensive care unit (SICU) patients and compared its performance against the existing insulin-infusion algorithm (named VA algorithm) used in our institution. METHODS: A computer program was developed to run the FM and VA algorithms. Forty eight patients, who were scheduled to have elective surgery, were randomly assigned to receive insulin infusion on the basis of either the FM or VA algorithm. On SICU admission, an insulin infusion was either continued from the operating room or initiated when the glucose level exceeded the target level of 140 mg/dL. Hourly blood glucose measurements were performed and entered into the computer program, which then prescribed the next insulin dose. The randomly assigned algorithm was applied for the first 8 hours of SICU stay, after which the VA algorithm was used. The number of episodes of hypoglycemia (glucose <60 mg/dL) and excessive hyperglycemia (>300 mg/dL) were noted. Additionally, the time required to bring the glucose level within target range (140 ± 20 mg/dL), the number of glucose measurements within the target range, glycemic variability, and insulin usage were analyzed and compared between the 2 algorithms. RESULTS: Patient demographics and starting glucose levels were similar between the groups. With the existing VA algorithm, 1 episode of severe hypoglycemia was observed. Three patients did not reach the target range within 8 hours. With the FM algorithm no hypoglycemia occurred, and all patients achieved the target range within 8 hours. Glycemic variability measured by the SD of mean glucose levels was 28% (95% confidence interval, 14% to 39%) lower for the FM algorithm (P < 0.001). The FM algorithm used 1.1 U/h less insulin than did the VA algorithm (P = 0.043). CONCLUSION: The novel computerized FM algorithm for glycemic control, which emulates physiologic biphasic insulin secretion, managed glucose better than the existing algorithm without any episodes of hypoglycemia. The FM algorithm had less glycemic variability and used less insulin when compared to the conventional clinical algorithm.

Explainable machine learning predictions to help anesthesiologists prevent hypoxemia during surgery

Hypoxemia causes serious patient harm, and while anesthesiologists strive to avoid hypoxemia during surgery, anesthesiologists are not reliably able to predict which patients will have intraoperative hypoxemia. Using minute by minute EMR data from fifty thousand surgeries we developed and tested a machine learning based system called Prescience that predicts real-time hypoxemia risk and presents an explanation of factors contributing to that risk during general anesthesia. Prescience improved anesthesiologists’ performance when providing interpretable hypoxemia risks with contributing factors. The results suggest that if anesthesiologists currently anticipate 15% of events, then with Prescience assistance they could anticipate 30% of events or an estimated additional 2.4 million annually in the US, a large portion of which may be preventable because they are attributable to modifiable factors. The prediction explanations are broadly consistent with the literature and anesthesiologists’ prior knowledge. Prescience can also improve clinical understanding of hypoxemia risk during anesthesia by providing general insights into the exact changes in risk induced by certain patient or procedure characteristics. Making predictions of complex medical machine learning models (such as Prescience) interpretable has broad applicability to other data-driven prediction tasks in medicine.

Explainable machine-learning predictions for the prevention of hypoxaemia during surgery

Although anaesthesiologists strive to avoid hypoxaemia during surgery, reliably predicting future intraoperative hypoxaemia is not possible at present. Here, we report the development and testing of a machine-learning-based system that predicts the risk of hypoxaemia and provides explanations of the risk factors in real time during general anaesthesia. The system, which was trained on minute-by-minute data from the electronic medical records of over 50,000 surgeries, improved the performance of anaesthesiologists by providing interpretable hypoxaemia risks and contributing factors. The explanations for the predictions are broadly consistent with the literature and with prior knowledge from anaesthesiologists. Our results suggest that if anaesthesiologists currently anticipate 15% of hypoxaemia events, with the assistance of this system they could anticipate 30%, a large portion of which may benefit from early intervention because they are associated with modifiable factors. The system can help improve the clinical understanding of hypoxaemia risk during anaesthesia care by providing general insights into the exact changes in risk induced by certain characteristics of the patient or procedure.An alert system based on machine learning and trained on surgical data from electronic medical records helps anaesthesiologists prevent hypoxaemia during surgery by providing interpretable real-time predictions.

The Effect of Intraoperative Blood Glucose Management on Postoperative Blood Glucose Levels in Noncardiac Surgery Patients

BACKGROUND:Postoperative hyperglycemia has been associated with poor surgical outcome. The effect of intraoperative glucose management on postoperative glucose levels and the optimal glycemic threshold for initiating insulin are currently unknown. METHODS:We performed a retrospective cohort study of surgery patients who required intraoperative glucose management with data extracted from electronic medical records. In patients who required glucose management, intraoperative glucose levels and insulin therapy were compared against postoperative glucose levels during 3 periods: first postoperative level within 1 hour, within the first 12 hours, and 24 hours of the postoperative period. Logistic regression models that adjusted for patient and surgical factors were used to determine the association between intraoperative glucose management and postoperative glucose levels. RESULTS:In 2440 patients who required intraoperative glucose management, an increase in mean intraoperative glucose level by 10 mg/dL was associated with an increase in postoperative glucose levels by 4.7 mg/dL (confidence interval [CI], 4.1–5.3; P < 0.001) for the first postoperative glucose measurement, 2.6 mg/dL (CI, 2.1–3.1; P < 0.001) for the mean first 12-hour postoperative glucose, and 2.4 mg/dL (CI, 2.0–2.9; P < 0.001) for the mean first 24-hour postoperative glucose levels (univariate analysis). Multivariate analysis showed that these effects depended on (interacted with) body mass index and diabetes status of the patient. Both diabetes status (regression coefficient = 12.2; P < 0.001) and intraoperative steroid use (regression coefficient = 10.2; P < 0.001) had a positive effect on elevated postoperative glucose levels. Intraoperative hyperglycemia (>180 mg/dL) was associated with postoperative hyperglycemia during the first 12 hours and the first 24 hours. However, interaction with procedure duration meant that this association was stronger for shorter surgeries. When compared with starting insulin for an intraoperative glucose threshold of 140 mg/dL thus avoiding hyperglycemia, initiation of insulin for a hyperglycemia threshold of 180 mg/dL was associated with an increase in postoperative glucose level (7 mg/dL; P < 0.001) and postoperative hyperglycemia incidence (odds ratio = 1.53; P = 0.01). CONCLUSIONS:A higher intraoperative glucose level is associated with a higher postoperative glucose level. Intraoperative hyperglycemia increases the odds for postoperative hyperglycemia. Adequate intraoperative glucose management by initiating insulin infusion when glucose level exceeds 140 mg/dL to prevent hyperglycemia is associated with lower postoperative glucose levels and fewer incidences of postoperative hyperglycemia. However, patient- and procedure-specific variable interactions make the relationship between intraoperative and postoperative glucose levels complicated.

Decisional practices and patterns of intraoperative glucose management in an academic medical center

OBJECTIVE To understand the decisional practices of anesthesia providers in managing intraoperative glucose levels. DESIGN This is a retrospective cohort study. SETTING Operating rooms in an academic medical center. PATIENTS Adult patients undergoing surgery. INTERVENTION Intraoperative blood glucose management based on an institutional protocol. MEASUREMENTS Glucose management data was extracted from electronic medical records to determine compliance to institutional glucose management protocol that prescribes hourly glucose measurements and insulin doses to maintain glucose levels between 100 to 140mg/dL. Effect of patient and surgery specific factors on compliance to glucose management protocol was explored. MAIN RESULTS In 1903 adult patients compliances to hourly glucose measurements was 72.5% and correct insulin adjustments was 12.4%. Insulin was under-dosed compared to the prescribed value by a mean of 0.85U/h (95% CI 0.76-0.95). Multivariate analysis showed that compliance to hourly glucose measurements decreased with increasing length of the procedure (OR=0.92 per hour, 95% CI 0.89-0.95) but increased with ASA status codes (OR=1.25 per ASA unit, 95% CI=1.06-1.49). Greater compliance to correct insulin adjustment was found in diabetic patients compared with non-diabetic patients (OR=1.31, 95% CI 1.09-1.55). On average, providers administered progressively more insulin with an additional 0.11U/h (95% CI=0.00-0.21] for every additional 10kg/m(2) of BMI and 0.20U/h (95% CI=0.01-0.39) less in diabetic patients than in non-diabetic patients. With the above practice pattern, the mean±SD of glucose level was 158±36mg/dL. Hypoglycemic (<60mg/dL) incident rate was 0.1% (9/8301 measurements) while hyperglycemic (>180mg/dL) incident rate was 28%. Glucose levels were within the target range (100-140mg/dL) only 28% of the time. CONCLUSIONS Low compliance and considerable variability in initiating and following institutional glucose management protocol were observed.

Association between acute phase perioperative glucose parameters and postoperative outcomes in diabetic and non-diabetic patients undergoing non-cardiac surgery

BACKGROUND The relationship between acute phase perioperative hyperglycemia and postoperative outcome is poorly understood. METHODS Retrospective cohort study of diabetic and non-diabetic adult patients undergoing non-cardiac surgery. Mean glucose and glycemic variability during the intraoperative and immediate postoperative periods were compared to length of stay, 30-day mortality, and postoperative complications. RESULTS . DIABETIC PATIENTS (N = 1096): Higher glycemic variability was associated with longer hospital length of stay (0.32 day per 10 mg/dL) and greater 30-day mortality risk (OR = 1.42). Higher mean glucose (OR = 1.07) and glycemic variability (OR = 1.11) were associated with higher risk of complications. NON-DIABETIC PATIENTS (N = 1012): Both higher mean glucose (0.29 day per 10 mg/dL) and higher glycemic variability (0.68 day per 10 mg/dL) were associated with longer hospital length of stay. Both higher mean glucose (OR = 1.13) and higher glycemic variability (OR = 1.21) were associated with greater risks of complications. CONCLUSIONS Poor acute phase perioperative glycemic control is associated with poor outcome, but differently in diabetic and non-diabetic patients suggesting different glycemic management strategies for the two patient groups.

Not Sweet At All

This case discusses the pharmacokinetic interaction between glipizide and trimethoprim-sulfamethoxazole (TMP/SMX), resulting in hypoglycemia. Glipizide is a cytochrome P450 2C9 substrate and TMP/SMX is a 2C9 inhibitor.