Generation and validation of a formula to calculate hemoglobin loss on a cohort of healthy adults subjected to controlled blood loss

Association between CFTR modulators and changes in iron deficiency markers in cystic fibrosis

BACKGROUND

Iron deficiency (ID) is a common extrapulmonary manifestation in cystic fibrosis (CF). CF transmembrane conductance regulator (CFTR) modulator therapies, particularly highly-effective modulator therapy (HEMT), have drastically improved health status in a majority of people with CF. We hypothesize that CFTR modulator use is associated with improved markers of ID.

METHODS

In a multicenter retrospective cohort study across 4 United States CF centers 2012-2022, the association between modulator therapies and ID laboratory outcomes was estimated using multivariable linear mixed effects models overall and by key subgroups. Summary statistics describe the prevalence and trends of ID, defined a priori as transferrin saturation (TSAT) <20 % or serum iron <60 μg/dL (<10.7 μmol/L).

RESULTS

A total of 568 patients with 2571 person-years of follow-up were included in analyses. Compared to off modulator therapy, HEMT was associated with +8.4 % TSAT (95 % confidence interval [CI], +6.3-10.6 %; p < 0.0001) and +34.4 μg/dL serum iron (95 % CI, +26.7-42.1 μg/dL; p < 0.0001) overall; +5.4 % TSAT (95 % CI, +2.8-8.0 %; p = 0.0001) and +22.1 μg/dL serum iron (95 % CI, +13.5-30.8 μg/dL; p < 0.0001) in females; and +11.4 % TSAT (95 % CI, +7.9-14.8 %; p < 0.0001) and +46.0 μg/dL serum iron (95 % CI, +33.3-58.8 μg/dL; p < 0.0001) in males. Ferritin was not different in those taking modulator therapy relative to off modulator therapy. Hemoglobin was overall higher with use of modulator therapy. The prevalence of ID was high throughout the study period (32.8 % in those treated with HEMT).

CONCLUSIONS

ID remains a prevalent comorbidity in CF, despite availability of HEMT. Modulator use, particularly of HEMT, is associated with improved markers for ID (TSAT, serum iron) and anemia (hemoglobin).

The incidence and risk factors for abnormal postoperative blood tests following primary total joint replacement

Aims

We aim to evaluate the usefulness of postoperative blood tests by investigating the incidence of abnormal results following total joint replacement (TJR), as well as identifying preoperative risk factors for abnormal blood test results postoperatively, especially pertaining to anaemia and acute kidney injury (AKI).

Methods

This is a retrospective cohort study of patients who had elective TJR between January and December 2019 at a tertiary centre. Data gathered included age at time of surgery, sex, BMI, American Society of Anesthesiologists (ASA) grade, preoperative and postoperative laboratory test results, haemoglobin (Hgb), white blood count (WBC), haematocrit (Hct), platelets (Plts), sodium (Na+), potassium (K+), creatinine (Cr), estimated glomerular filtration rate (eGFR), and Ferritin (ug/l). Abnormal blood tests, AKI, electrolyte imbalance, anaemia, transfusion, reoperation, and readmission within one year were reported.

Results

The study included 2,721 patients with a mean age of 69 years, of whom 1,266 (46.6%) were male. Abnormal postoperative bloods were identified in 444 (16.3%) patients. We identified age (≥ 65 years), female sex, and ASA grade ≥ III as risk factors for developing abnormal postoperative blood tests. Preoperative haemoglobin (≤ 127 g/dl) and packed cell volume (≤ 0.395 l/l) were noted to be significant risk factors for postoperative anaemia, and potassium (≤ 3.7 mmol/l) was noted to be a significant risk factor for AKI.

Conclusion

The costs outweigh the benefits of ordering routine postoperative blood tests in TJR patients. Clinicians should risk-stratify their patients and have a lower threshold for ordering blood tests in patients with abnormal preoperative haemoglobin (≤ 127 g/l), blood loss > 300 ml, chronic kidney disease, ASA grade ≥ III, and clinical concern.

Artificial Intelligence and Machine Learning in Patient Blood Management: A Scoping Review

Machine learning (ML) and artificial intelligence (AI) are widely used in many different fields of modern medicine. This narrative review gives, in the first part, a brief overview of the methods of ML and AI used in patient blood management (PBM) and, in the second part, aims at describing which fields have been analyzed using these methods so far. A total of 442 articles were identified by a literature search, and 47 of them were judged as qualified articles that applied ML and AI techniques in PBM. We assembled the eligible articles to provide insights into the areas of application, quality measures of these studies, and treatment outcomes that can pave the way for further adoption of this promising technology and its possible use in routine clinical decision making. The topics that have been investigated most often were the prediction of transfusion (30%), bleeding (28%), and laboratory studies (15%). Although in the last 3 years a constantly increasing number of questions of ML in PBM have been investigated, there is a vast scientific potential for further application of ML and AI in other fields of PBM.