Cellulitis

Using the electronic medical records system at each site, we searched the following ICD-9-CM codes to identify possible visits for hypoglycemia: 250.3 (diabetes with other coma), 250.8 (diabetes with other specified manifestations) 251.0 (hypoglycemic coma), 251.1 (other specified hypoglycemia), 251.2 (hypoglycemia, unspecified), 270.3 (leucine-induced hypoglycemia), 775.0 (hypoglycemia in an infant born to a diabetic mother), 775.6 (neonatal hypoglycemia), and 962.3 (poisoning by insulins and antidiabetic agents).
Given the diversity of potential ICD-9-CM codes, we searched this broad range of codes and in all diagnosis fields (up to ten listed) in an attempt to capture all possible ED hypoglycemia visits. For admitted patients, we examined only ED-based codes, to avoid inclusion of incident hypoglycemia that occurred during inpatient hospitalization. In cases where multiple candidate codes were present, we recorded only the first-listed code. The exception to this was for the more ambiguous codes 250.3 and 250.8, for which we preferentially recorded any of the other candidate codes if present. We based this strategy on detailed examination of the ICD-9-CM coding manual [9 ], review of the experience from previously reported approaches [10 (link)-14 (link)], and discussion with coding experts.
The code 250.8 may be used for other specific diabetes-associated complications in addition to hypoglycemia, including: 259.8 (secondary diabetic glycogenosis), 272.7 (diabetic lipidosis), 707.xx (ulcers of the lower extremity), 709.3 (Oppenheim-Urbach syndrome), and 730.0–730.2, 731.8 (osteomyelitis). Based on discussion with coding experts, we determined that 681.xx (cellulitis of fingers/toes), 682.xx (other cellulitis), and 686.9x (local skin infection) may also be utilized as a co-diagnoses for 250.8, although not specifically mentioned in the manual. We prospectively proposed the coding algorithm displayed in Figure 1 and validated its accuracy through chart review.
We identified all ED visits with candidate ICD-9-CM codes between July 1, 2005 and June 30, 2006 at each site, and obtained written ED charts. For patients with multiple ED visits during the data collection period, we requested only the first visit to avoid overrepresentation by certain patients. Trained research staff abstracted all charts using a standardized form, and the research group met weekly to discuss data collection and resolve abstraction issues. Additionally, two reviewers independently abstracted 10% of charts to evaluate inter-rater agreement in data collection. To enhance the reliability of our chart review, we abstracted only charts with complete ED triage assessment, nursing notes, and emergency physician notes and considered all other charts incomplete.

We investigated whether the neurological and psychiatric sequelae of COVID-19 were affected by the severity of the illness. The incidence of outcomes was estimated separately in four subgroups: first, in those who had required hospitalisation within a time window from 4 days before their COVID-19 diagnosis (taken to be the time it might take between clinical presentation and confirmation) to 2 weeks afterwards; second, in those who had not required hospitalisation during that window; third, in those who had been admitted to an intensive therapy unit (ITU) during that window; and fourth, in those who were diagnosed with delirium or other forms of altered mental status during that window; we use the term encephalopathy to describe this group of patients (appendix p 5).17 (link), 18
Differences in outcome incidence between these subgroups might reflect differences in their baseline characteristics. Therefore, for each outcome, we estimated the HR between patients requiring hospitalisation (or ITU) and a matched cohort of patients not requiring hospitalisation (or ITU), and between patients with encephalopathy and a matched cohort of patients without encephalopathy. Finally, HRs were calculated for patients who had not required hospitalisation for COVID-19, influenza, or other respiratory tract infections.
To provide benchmarks for the incidence and risk of neurological and psychiatric sequelae, patients after COVID-19 were compared with those in four additional matched cohorts of patients diagnosed with health events selected to represent a range of acute presentations during the same time period. These additional four index events were skin infection, urolithiasis, fracture of a large bone, and pulmonary embolism. More details are presented in the appendix (pp 5–6).
We assessed the robustness of the differences in outcomes between cohorts by repeating the analysis in three scenarios: one including patients who had died by the time of the analysis, another restricting the COVID-19 diagnoses to patients who had a positive RNA or antigen test (and using antigen test as an index event), and another comparing the rates of sequelae of patients with COVID-19 with those observed in patients with influenza before the pandemic (ie, in 2019 or 2018). Details of these analyses are provided in the appendix (p 6).
Finally, to test whether differences in sequelae between cohorts could be accounted for by differences in extent of follow-up, we counted the average number of health visits that each cohort had during the follow-up period.

These analyses were performed with a subpopulation of Transitional Care Study 3 (TRICA; ClinicalTrials.gov NCT03923127)—a single-center study on wearable monitoring in postoperative patients in a tertiary hospital [17 (link),18 (link)]. All adult patients scheduled for major abdominal oncological or bariatric surgery from April 2019 to August 2020 who were willing and able to sign informed consent were eligible for participation. Patients who met any of the following criteria were not included: being pregnant or breastfeeding, having an allergy to tissue adhesives, having an antibiotic-resistant skin infection, having an active implantable device, or having any skin condition at the area of application of the devices. This subanalysis describes 68 postoperative patients, and inclusion into this subanalysis for accuracy of the wearable sensor was based on the availability of research personnel and real-time data logging equipment.