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IMPORTANCE Accurate surgical scheduling affects patients, clinical staff, and use of physical resources. Although numerous retrospective analyses have suggested a potential for improvement, the real-world outcome of implementing a machine learning model to predict surgical case duration appears not to have been studied. OBJECTIVESTo assess accuracy and real-world outcome from implementation of a machine learning model that predicts surgical case duration. DESIGN, SETTING, AND PARTICIPANTSThis randomized clinical trial was conducted on 2 surgical campuses of a cancer specialty center. Patients undergoing colorectal and gynecology surgery at Memorial Sloan Kettering Cancer Center who were scheduled more than 1 day before surgery between April 7, 2018, and June 25, 2018, were included. The randomization process included 29 strata (11 gynecological surgeons at 2 campuses and 7 colorectal surgeons at a single campus) to ensure equal chance of selection for each surgeon and each campus. Patients undergoing more than 1 surgery during the study's timeframe were enrolled only once. Data analyses took place from July 2018 to November 2018. INTERVENTIONSCases were assigned to machine learning-assisted surgical predictions 1 day before surgery and compared with a control group. MAIN OUTCOMES AND MEASURESThe primary outcome measure was accurate prediction of the duration of each scheduled surgery, measured by (arithmetic) mean (SD) error and mean absolute error. Effects on patients and systems were measured by start time delay of following cases, the time between cases, and the time patients spent in presurgical area.RESULTS A total of 683 patients were included (mean [SD] age, 55.8 [13.8] years; 566 women [82.9%]); 72 were excluded. Of the 683 patients included, those assigned to the machine learning algorithm had significantly lower mean (SD) absolute error (control group, 59.3 [72] minutes; intervention group, 49.5 [66] minutes; difference, −9.8 minutes; P = .03) compared with the control group. Mean start-time delay for following cases (patient wait time in a presurgical area), dropped significantly: 62.4 minutes (from 70.2 minutes to 7.8 minutes) and 16.7 minutes (from 36.9 minutes to 20.2 minutes) for patients receiving colorectal and gynecology surgery, respectively. The overall mean (SD) reduction of wait time was 33.1 minutes per patient (from 49.4 minutes to 16.3 minutes per patient). Improved accuracy did not adversely inflate time between cases (surgeon wait time). There was marginal improvement (1.5 minutes, from a mean of 70.6 to 69.1 minutes) in time between the end of cases and start of to-follow cases using the predictive model, compared with the control group. Patients spent a mean of 25.2 fewer minutes in the facility before surgery (173.3 minutes vs 148.1 minutes), indicating a potential benefit vis-à-vis available resources for other patients before and after surgery.CONCLUSIONS AND RELEVANCE Implementing machine learning-generated predictions for surgical case durations may improve case duration accura...

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Objectives In the Fluid and Catheter Treatment Trial (FACTT) of the National Institutes of Health Acute Respiratory Distress Syndrome Network, a conservative fluid protocol (FACTT Conservative) resulted in a lower cumulative fluid balance and better outcomes than a liberal fluid protocol (FACTT Liberal). Subsequent Acute Respiratory Distress Syndrome Network studies used a simplified conservative fluid protocol (FACTT Lite). The objective of this study was to compare the performance of FACTT Lite, FACTT Conservative, and FACTT Liberal protocols. Design Retrospective comparison of FACTT Lite, FACTT Conservative, and FACTT Liberal. Primary outcome was cumulative fluid balance over 7 days. Secondary outcomes were 60-day adjusted mortality and ventilator-free days through day 28. Safety outcomes were prevalence of acute kidney injury and new shock. Setting ICUs of Acute Respiratory Distress Syndrome Network participating hospitals. Patients Five hundred three subjects managed with FACTT Conservative, 497 subjects managed with FACTT Liberal, and 1,124 subjects managed with FACTT Lite. Interventions Fluid management by protocol. Measurements and Main Results Cumulative fluid balance was 1,918 ± 323 mL in FACTT Lite, −136 ±491 mL in FACTT Conservative, and 6,992 ± 502 mL in FACTT Liberal (p < 0.001). Mortality was not different between groups (24% in FACTT Lite, 25% in FACTT Conservative and Liberal, p = 0.84). Ventilator-free days in FACTT Lite (14.9 ±0.3) were equivalent to FACTT Conservative (14.6±0.5) (p = 0.61) and greater than in FACTT Liberal (12.1 ±0.5, p < 0.001 vs Lite). Acute kidney injury prevalence was 58% in FACTT Lite and 57% in FACTT Conservative (p = 0.72). Prevalence of new shock in FACTT Lite (9%) was lower than in FACTT Conservative (13%) (p = 0.007 vs Lite) and similar to FACTT Liberal (11%) (p = 0.18 vs Lite). Conclusions FACTT Lite had a greater cumulative fluid balance than FACTT Conservative but had equivalent clinical and safety outcomes. FACTT Lite is an alternative to FACTT Conservative for fluid management in Acute Respiratory Distress Syndrome.

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Several isoforms of neuronal nitric oxide synthase (nNOS) have been identified. Antisense approaches have been developed which can selectively down-regulate nNOS-1, which corresponds to the full-length nNOS originally cloned from the brain, and nNOS-2, a truncated form lacking two exons which is generated by alternative splicing, as demonstrated by decreases in mRNA levels. Antisense treatment also lowers nNOS enzymatic activity. Downregulation of nNOS-1 prevents the development of morphine tolerance. Whereas morphine analgesia is lost in control and mismatch-treated mice given daily morphine injections for 5 days, mice treated with antisense probes targeting nNOS-1 show no decrease in their morphine sensitivity over the same time period. Conversely, an antisense probe selectively targeting nNOS-2 blocks morphine analgesia, shifting the morphine dose-response curve over 2-fold to the right. Both systems are active at the spinal and the supraspinal levels. An antisense targeting inducible NOS is inactive. Studies with N G -nitro-L-arginine, which does not distinguish among NOS isoforms, indicate that the facilitating nNOS-2 system predominates at the spinal level while the inhibitory nNOS-1 system is the major supraspinal nNOS system. Thus, antisense mapping distinguishes at the functional level two isoforms of nNOS with opposing actions on morphine actions. The ability to selectively down-regulate splice variants opens many areas in the study of nNOS and other proteins.