Diagnostic accuracy of the anion gap to identify toxic lithium concentrations: a single-center retrospective observational study.

INTRODUCTION: Lithium exhibits a narrow margin between therapeutic doses and toxic blood concentrations, which can pose a substantial risk of toxic effects. Reportedly, lithium toxicity may be associated with a reduced anion gap; however, the precise relationship remains unclear. This study examined several different anion gap calculation methods to detect toxic lithium concentrations without directly measuring blood lithium concentrations. METHODS: Our retrospective study analyzed blood samples collected for lithium concentration measurements. The anion gap was determined using three different methods, both with and without albumin and lactate concentration corrections. Samples were categorized into two groups based on lithium concentration (<1.5 or ≥1.5 mmol/L), and anion gap values were compared. Correlation and logistic regression analyses were used to assess the relationship between each anion gap indicator and lithium concentration. Receiver operating characteristic curves were used for diagnostic analysis. RESULTS: Overall, 24 measurements were collected, with 41.7% of samples falling within the toxic range. The high-lithium concentration group exhibited significantly smaller anion gaps. Correlation and logistic regression analyses revealed a significant association between anion gap values and lithium concentrations. Areas under the receiver operating characteristic curve were: conventional anion gap 0.77 (95% CI: 0.55-0.94); albumin-corrected anion gap 0.85 (95% CI: 0.66-1.00); and both albumin- and lactate-corrected anion gap 0.86 (95% CI: 0.66-1.00). DISCUSSION: The anion gap is calculated as the difference between measured cations and anions. Accumulation of lithium (a cation) may decrease measured cations and decrease the calculated anion gap. Abnormal albumin and lactate concentrations may also alter the anion gap and affect its usefulness as a diagnostic marker for elevated serum lithium concentrations. A negative likelihood ratio of 0.1 suggests that the anion gap might be valuable in excluding toxicity. CONCLUSIONS: The corrected anion gap, accounting for albumin and lactate concentrations, may be beneficial in suggesting the possibility of toxic lithium concentrations.

Impact on outcomes of measuring lactates prior to ICU in unselected heterogeneous critically ill patients: A propensity score analysis.

BACKGROUND: Elevated blood lactate levels were reported as effective predictors of clinical outcome and mortality in ICU. However, there have been no studies simply comparing the timing of measuring lactates before vs. after ICU admission. METHODS: A total of 19,226 patients with transfer time ≤ 24 hr were extracted from the Medical Information Mart for Intensive Care IV database (MIMIC-IV). After 1:1 propensity score matching, the patients were divided into two groups: measuring lactates within 3 hr before (BICU group, n = 4,755) and measuring lactate within 3 hr after ICU admission(AICU group, n = 4,755). The primary and secondary outcomes were hospital mortality, hospital 28-day mortality, ICU mortality, ICU length of stay (LOS), hospital LOS, and restricted mean survival time (RMST). RESULTS: Hospital, hospital 28-day, and ICU mortality were significantly higher in AICU group (7.0% vs.9.8%, 6.7% vs. 9.4%, and 4.6% vs.6.7%, respectively, p<0.001 for all) Hospital LOS and ICU LOS were significantly longer in AICU group (8.4 days vs. 9.0 days and 3.0 days vs. 3.5 days, respectively, p<0.001 for both). After adjustment for predefined covariates, a significant association between the timing of measuring lactate and hospital mortality was observed in inverse probability treatment weight (IPTW) multivariate regression, doubly robust multivariate regression, and multivariate regression models (OR, 0.96 [95%CI, 0.95-0.97], OR 0.52 [95%CI, 0.46-0.60], OR 0.66 [95%CI, 0.56-0.78], respectively, p<0.001 for all), indicating the timing as a significant risk-adjusted factor for lower hospital mortality. The difference (BICU-AICU) of RMST at 28- days after ICU admission was 0.531 days (95%CI, 0.002-1.059, p<0.05). Placement of A-line and PA-catheter, administration of intravenous antibiotics, and bolus fluid infusion during the first 24-hr in ICU were significantly more frequent and faster in the BICU vs AICU group (67.6% vs. 51.3% and 126min vs.197min for A-line, 19.6% vs.13.2% and 182min vs. 274min for PA-catheter, 77.5% vs.67.6% and 109min vs.168min for antibiotics, and 57.6% vs.51.6% and 224min vs.278min for bolus fluid infusion, respectively, p<0.001 for all). Additionally, a significant indirect effect was observed in frequency (0.19879 [95% CI, 0.14061-0.25697] p<0.001) and time (0.07714 [95% CI, 0.22600-0.13168], p<0.01) of A-line replacement, frequency of placement of PA-catheter (0.05614 [95% CI, 0.04088-0.07140], p<0.001) and frequency of bolus fluid infusion (0.02193 [95%CI, 0.00303-0.04083], p<0.05). CONCLUSIONS: Measuring lactates within 3 hr prior to ICU might be associated with lower hospital mortality in unselected heterogeneous critically ill patients with transfer time to ICU ≤ 24hr, presumably due to more frequent and faster therapeutic interventions.

Intrafamilial Transmission of Parechovirus A and Enteroviruses in Neonates and Young Infants.

BACKGROUND: Parechovirus A (PeV-A) is an important cause of sepsis and meningoencephalitis in neonates and young infants. Thus, identifying the source of PeV-A is essential for prevention; however, little is known regarding the spread of PeV-A among family members of PeV-A-infected neonates and young infants. METHODS: In this prospective study, we evaluated stool samples from family members of PeV-A-infected neonates and infants younger than 4 months who presented with sepsis, meningoencephalitis, or both in Niigata, Japan, in 2016. Because of a simultaneous outbreak, enteroviruses (EVs) were also evaluated during this period. Real-time polymerase chain reaction followed by sequence analysis was used for viral diagnosis using serum and/or cerebrospinal fluid samples. RESULTS: Among 54 febrile patients, the stool samples of 14 (26%) and 12 (22%) patients tested positive for PeV-A and EV, respectively. Stool samples from 54 family members (38 adults and 16 children) of 12 PeV-A-infected patients were available. The rate of PeV-A positivity in these samples was higher among the children (88% [14 of 16]) than the adults (34% [13 of 38]). Among family members with a PeV-A-positive stool sample, 29% (4 of 14) of the children and 77% (10 of 13) of the adults were asymptomatic. Similarly, among 53 stool samples from family members (31 adults and 22 children) of 11 EV-infected patients, the rate of EV positivity in the stool samples was higher among the children (91% [20 of 22]) than among the adults (42% [13 of 31]). The asymptomatic-patient rates were 45% (9 of 20) among the children and 85% (11 of 13) among the adults in family members with EV-positive stool. CONCLUSIONS: Similar to EVs, PeV-A was detected frequently in stool samples from family members of PeV-A-infected patients. Among family members with PeV-A-positive stool, adults were more likely than children to be asymptomatic and therefore could be an important source of PeV-A infection.