A simple electronic medical record-based predictors of illness severity in sepsis (sepsis) score.

OBJECTIVE: Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort. DESIGN: A simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors) using a population-based retrospective cohort study. SETTING: Internal medicine services across four academic teaching hospitals in Toronto, Canada from April 2010-March 2015 (primary cohort) and 2015-2019 (secondary cohort). PATIENTS: We identified patients admitted with sepsis based upon receipt of antibiotics and positive cultures. MEASUREMENTS AND MAIN RESULTS: The primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a secondary cohort (2015-2019) of hospitalized patients receiving antibiotics. Our primary cohort included 1,890 patients with a median age of 72 years (IQR: 56-83). 9% died during hospitalization, 18.6% were admitted to ICU, and mean LOS was 12.7 days (SD: 21.5). In the primary and secondary (2015-2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay > 14 days, were similar between scores, in the primary and secondary cohorts. All scores had comparable calibration for predicting mortality. CONCLUSIONS: An EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score's simplicity, it may prove a useful tool for clinical and research applications.

Osmotic Demyelination Syndrome in Patients Hospitalized with Hyponatremia.

BACKGROUND: Osmotic demyelination syndrome (ODS) is a rare but potentially devastating neurologic complication of hyponatremia. The primary objective of this study was to identify the proportion of patients who developed ODS in a large, contemporary, multicenter cohort of patients admitted to the hospital with hyponatremia. METHODS: We conducted a multicenter cohort study of patients admitted with hyponatremia at five academic hospitals in Toronto, Ontario, Canada, between April 1, 2010, and December 31, 2020. All adult patients presenting with hyponatremia (serum sodium level 8 mmol/l in any 24-hour period). RESULTS: Our cohort included 22,858 hospitalizations with hyponatremia. Approximately 50% were women, the average age was 68 years, and mean initial serum sodium was 125 mmol/l (standard deviation, 4.6), including 11.9% with serum sodium from 110 to 119 mmol/l and 1.2% with serum sodium less than 110 mmol/l. Overall, rapid correction of serum sodium occurred in 3632 (17.7%) admissions. Twelve patients developed ODS (0.05%). Seven (58%) patients who developed ODS did not have rapid correction of serum sodium. CONCLUSIONS: In this large multicenter study of patients with hyponatremia, rapid correction of serum sodium was common (n=3632 [17.7%]), but ODS was rare (n=12 [0.05%]). Future studies with a higher number of patients with ODS are needed to better understand potential causal factors for ODS.

Assessing the quality of clinical and administrative data extracted from hospitals: the General Medicine Inpatient Initiative (GEMINI) experience.

OBJECTIVE: Large clinical databases are increasingly used for research and quality improvement. We describe an approach to data quality assessment from the General Medicine Inpatient Initiative (GEMINI), which collects and standardizes administrative and clinical data from hospitals. METHODS: The GEMINI database contained 245 559 patient admissions at 7 hospitals in Ontario, Canada from 2010 to 2017. We performed 7 computational data quality checks and iteratively re-extracted data from hospitals to correct problems. Thereafter, GEMINI data were compared to data that were manually abstracted from the hospital's electronic medical record for 23 419 selected data points on a sample of 7488 patients. RESULTS: Computational checks flagged 103 potential data quality issues, which were either corrected or documented to inform future analysis. For example, we identified the inclusion of canceled radiology tests, a time shift of transfusion data, and mistakenly processing the chemical symbol for sodium ("Na") as a missing value. Manual validation identified 1 important data quality issue that was not detected by computational checks: transfusion dates and times at 1 site were unreliable. Apart from that single issue, across all data tables, GEMINI data had high overall accuracy (ranging from 98%-100%), sensitivity (95%-100%), specificity (99%-100%), positive predictive value (93%-100%), and negative predictive value (99%-100%) compared to the gold standard. DISCUSSION AND CONCLUSION: Computational data quality checks with iterative re-extraction facilitated reliable data collection from hospitals but missed 1 critical quality issue. Combining computational and manual approaches may be optimal for assessing the quality of large multisite clinical databases.