IMPORTANCEThe National COVID Cohort Collaborative (N3C) is a centralized, harmonized, highgranularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.OBJECTIVES To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. DESIGN, SETTING, AND PARTICIPANTSIn a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). MAIN OUTCOMES AND MEASURESPatient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. RESULTSThe cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472(18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, (continued) Key Points Question In a US data resource large enough to adjust for multiple confounders, what risk factors are associated with COVID-19 severity and severity trajectory over time, and can machine learning models predict clinical severity? Findings In this cohort study of 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized and 6565 (20.2%) were severely ill, and first-day machine learning models accurately predicted clinical severity. Mortality was 11.6%

Study Design. Retrospective cohort analysis of a nationwide administrative database.Objective. The aim of this study was to analyze the association between cannabis abuse and serious adverse events following elective spine surgery. Summary of Background Data. Cannabis is the most popular illicit drug in the United States, and its use has been increasing in light of state efforts to decriminalize and legalize its use for both medical and recreational purposes. Its legal status has long precluded extensive research into its adverse effects, and to date, little research has been done on the sequelae of cannabis on surgical patients, particularly in spine surgery. Methods. The 2012-2015 Nationwide Inpatient Sample was queried for all patients undergoing common elective spine surgery procedures. These patients were then grouped by the presence of concurrent diagnosis of cannabis use disorder and compared with respect to various peri-and postoperative complications, all-cause mortality, discharge disposition, length of stay, and hospitalization costs. Propensity score matching was utilized to control for potential baseline confounders.Results. A total of 423,978 patients met inclusion/exclusion criteria, 2393 (0.56%) of whom had cannabis use disorder. After controlling for baseline characteristics and comorbid tobacco use, these patients similar inpatient mortality, but higher rates of perioperative thromboembolism (odds ratio [OR] 2.2; 95% confidence interval [CI] 1.2-4.0; P ¼ 0.005), respiratory complications (OR 2.0; 95% CI 1.4 -2.9; P < 0.001), neurologic complications such as stroke and anoxic brain injury (OR 2.9; 95% CI 1.2-7.5; P ¼ 0.007), septicemia/sepsis (OR 1.5; 95% CI 1.0 to 2.5; P ¼ 0.031), and nonroutine discharge (P < 0.001), as well as increased lengths of stay (7.1 vs. 5.2 days, P < 0.001) and hospitalization charges ($137,631.30 vs. $116,112.60, P < 0.001). Conclusion. Cannabis abuse appears to be associated with increased perioperative morbidity among spine surgery patients. Physicians should ensure that a thorough preoperative drug use history is taken, and that affected patients be adequately informed of associated risks.

BACKGROUND Autologous bone removed during craniectomy is often the material of choice in cranioplasty procedures. However, when the patient's own bone is not appropriate (infection and resorption), an alloplastic graft must be utilized. Common options include titanium mesh and polyetheretherketone (PEEK)-based custom flaps. Often, neurosurgeons must decide whether to use a titanium or custom implant, with limited direction from the literature. OBJECTIVE To compare surgical outcomes of synthetic cranioplasties performed with titanium or vs custom implants. METHODS Ten-year retrospective comparison of patients undergoing synthetic cranioplasty with titanium or custom implants. RESULTS A total of 82 patients were identified for review, 61 (74.4%) receiving titanium cranioplasty and 21 (25.6%) receiving custom implants. Baseline demographics and comorbidities of the 2 groups did not differ significantly, although multiple surgical characteristics did (size of defect, indication for craniotomy) and were controlled for via a 2:1 mesh-to-custom propensity matching scheme in which 36 titanium cranioplasty patients were compared to 18 custom implant patients. The cranioplasty infection rate of the custom group (27.8%) was significantly greater (P = .005) than that of the titanium group (0.0%). None of the other differences in measured complications reached significance. Discomfort, a common cause of reoperation in the titanium group, did not result in reoperation in any of the patients receiving custom implants. CONCLUSION Infection rates are higher among patients receiving custom implants compared to those receiving titanium meshes. The latter should be informed of potential postsurgical discomfort, which can be managed nonsurgically and is not associated with return to the operating room.

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