Short Timeframe Prediction of Kidney Failure among Patients with Advanced Chronic Kidney Disease.

BACKGROUND: Development of a short timeframe (6-12 months) kidney failure risk prediction model may serve to improve transitions from advanced chronic kidney disease (CKD) to kidney failure and reduce rates of unplanned dialysis. The optimal model for short timeframe kidney failure risk prediction remains unknown. METHODS: This retrospective study included 1757 consecutive patients with advanced CKD (mean age 66 years, estimated glomerular filtration rate 18 mL/min/1.73 m2). We compared the performance of Cox regression models using (a) baseline variables alone, (b) time-varying variables and machine learning models, (c) random survival forest, (d) random forest classifier in the prediction of kidney failure over 6/12/24 months. Performance metrics included area under the receiver operating characteristic curve (AUC-ROC) and maximum precision at 70% recall (PrRe70). Top-performing models were applied to 2 independent external cohorts. RESULTS: Compared to the baseline Cox model, the machine learning and time-varying Cox models demonstrated higher 6-month performance [Cox baseline: AUC-ROC 0.85 (95% CI 0.84-0.86), PrRe70 0.53 (95% CI 0.51-0.55); Cox time-varying: AUC-ROC 0.88 (95% CI 0.87-0.89), PrRe70 0.62 (95% CI 0.60-0.64); random survival forest: AUC-ROC 0.87 (95% CI 0.86-0.88), PrRe70 0.61 (95% CI 0.57-0.64); random forest classifier AUC-ROC 0.88 (95% CI 0.87-0.89), PrRe70 0.62 (95% CI 0.59-0.65)]. These trends persisted, but were less pronounced, at 12 months. The random forest classifier was the highest performing model at 6 and 12 months. At 24 months, all models performed similarly. Model performance did not significantly degrade upon external validation. CONCLUSIONS: When predicting kidney failure over short timeframes among patients with advanced CKD, machine learning incorporating time-updated data provides enhanced performance compared with traditional Cox models.

Characterizing ability of serum potassium (K) and the serum K reference interval to flag hypokalemia or hyperkalemia as observed in plasma: A simulation study.

OBJECTIVES: Serum potassium (K) exhibits a positive shift relative to plasma K due to a variable amount of K release associated with clotting. Because of this variation, plasma K results outside of the reference interval (RI) for plasma (hypokalemia or hyperkalemia) in individual samples may not produce classification-concordant results in serum according to the serum RI. We examined this premise from a theoretical standpoint by simulation. DESIGN & METHODS: We used textbook K reference intervals for plasma (PRI = 3.4-4.5 mmol/L) and serum (SRI = 3.5-5.1 mmol/L). The difference between PRI and SRI is characterized by a normal distribution: serum K = plasma K + 0.35 ± 0.308 mmol/L. This transformation was applied by simulation to an observed patient data distribution for plasma K to generate a corresponding theoretical serum K distribution. Individual samples were tracked for comparison with respect to classification (below, within, above RI) for plasma and serum. RESULTS: Primary data were an all-comers plasma K patient distribution (n = 41,768; median = 4.1 mmol/L; 7.1% below PRI (hypokalemia); 15.5% above PRI (hyperkalemia)). Simulation to obtain the associated serum K yielded a right-shifted distribution (median = 4.4 mmol/L; 4.8% below SRI; 10.8% above SRI). Sensitivity for detection in serum (flagged below SRI) for samples originating as hypokalemic in plasma was 45.7% (specificity = 98.3%). Sensitivity for detection in serum (flagged above SRI) for samples originating as hyperkalemic in plasma was 56.6% (specificity = 97.6%). CONCLUSIONS: Simulation results indicate that serum K should best be thought of as an inferior substitute marker for plasma K. These results follow simply from the variable component of serum K compared to plasma K. Plasma should be the preferred specimen type for K assessment.

The Independent Association of Plasma and Red Blood Cell Zinc Concentrations with Long-Term Outcomes of Hospitalized Patients.

Background: Plasma and RBC zinc values are unrelated in hospitalized patients. The independent association of these values with important patient outcomes is unknown. Objectives: Measure the independent association of plasma and RBC zinc with outcomes in hospitalized patients. Methods: Plasma and RBC zinc concentrations were prospectively measured within 48 h of hospitalization in consenting patients. Data were linked deterministically with population-based health administrative data to measure each association of zinc measures with 2 outcomes (time to death from any cause and likelihood of death or urgent readmission to hospital within 30-d of discharge) after adjusting for validated outcome risk scores. Results: In total, 250 people admitted to medical services were studied. Patients were ill with a 1-y baseline expected death risk (IQR) of 19.9% (6.3%-37.2%). The observed 1-y and 2-y all-cause death risks were 24.5% (95% CI: 19.6%, 30.3%) and 33.2% (95% CI: 27.3%, 39.9%), respectively. Death risk increased significantly as plasma zinc concentrations decreased (P = 0.0001). This association persisted even after adjusting for the baseline expected death risk (P = 0.02) with every 2-µmol/L decrease in plasma zinc concentrations being independently associated with, on average, a 35% increase in the death risk. RBC zinc concentrations were not associated with the death risk. Neither plasma nor RBC zinc concentrations were significantly associated with the 30-d death or urgent readmission rate. Conclusions: Plasma, but not RBC, zinc concentrations are independently associated with the all-cause death risk in hospitalized medical patients. Further study is required to determine whether this association is causal and identify its potential causal pathways. Curr Dev Nutr 2023;x:xx.

A Diagnostic Dilemma "Cured" by Dialysis: An Educational Case Report.

Rationale: The differential diagnosis for a patient with high-anion-gap metabolic acidosis (HAGMA) is broad; lactic acidosis is an important entity to screen for and treat. An elevated serum lactate is often used as a marker of inadequate tissue perfusion in critically ill patients but can also be indicative of decreased lactate utilization or poor hepatic clearance. Investigating for the underlying cause such as diabetic ketoacidosis, malignancy, or culprit medications is essential to establish the diagnosis and treatment plan. Presenting concerns of the patient: A 60-year-old man with a history of substance use and end-stage kidney disease treated with hemodialysis presented to hospital with confusion, altered level of consciousness, and hypothermia. Initial laboratory investigations were significant for a severe HAGMA with elevated serum lactate and ß-hydroxybutyrate levels, but toxicology screen was negative, and there was no clear underlying precipitant. Urgent hemodialysis was arranged to mitigate his severe acidosis. Diagnoses: He had an initial single dialysis treatment for 4 hours, with posthemodialysis labs showing significant improvement in his acidosis, serum lactate level, and clinical status (cognition, hypothermia). Given this rapid resolution, a sample from his predialysis blood work was sent for analysis of plasma metformin and returned significantly elevated at 60 mcg/mL (therapeutic range 1-2 mcg/mL). Interventions and outcomes: On careful medication reconciliation in the dialysis unit, the patient stated he had never heard of the medication metformin, and there was no record of a filled prescription at his pharmacy. Given his living situation with shared accommodations, it was presumed that he had taken medications that were prescribed to a roommate. Several of his other medications including his antihypertensives were subsequently given after dialysis on dialysis days to improve adherence. Teaching points: Maintain a broad differential diagnosis for patients presenting with a clinical syndrome consistent with an acute toxicity even if no culprit medications are identifiable on history, especially in patients with a suggestive social history.Anion-gap metabolic acidosis (AGMA) is common in hospitalized patients but sometimes requires further history and/or confirmatory testing to elucidate the root cause underlying typical causes of AGMA such as lactic acidosis or ketoacidosis.The main treatment of metformin toxicity is resuscitation and supportive care; however, metformin's biochemical properties make it readily dialyzable via either diffusion or convection.The Extracorporeal Treatments In Poisoning group recommends hemodialysis for metformin toxicity when there is a serum lactate >20 mmol/L, a blood pH <7.0, a failure of standard therapy, end-organ damage (hepatic or renal insufficiency), or a decreased level of consciousness.

Justification: Le diagnostic différentiel d'un patient présentant une acidose métabolique à trou anionique élevé (AMTAE) est large. L'acidose lactique est une entité importante à dépister et à traiter. Un taux élevé de lactate sérique est fréquemment utilisé comme marqueur d'une perfusion tissulaire inadéquate chez les patients gravement malades, mais il peut également indiquer une utilization réduite du lactate ou une insuffisance hépatique. La recherche de la cause sous-jacente (acidocétose diabétique, malignité ou médicaments responsables) est essentielle pour établir le diagnostic et décider du plan de traitement. Présentation du cas: Un homme de 60 ans atteint d'insuffisance rénale terminale traitée par hémodialyse et ayant des antécédents de toxicomanie qui s'était présenté à l'hôpital confus, avec une altération de l'état de conscience et souffrant d'hypothermie. Les premières analyses de laboratoire ont révélé une grave acidose métabolique à trou anionique élevé et une concentration élevée de lactate sérique et de ß-hydroxybutyrate. Cependant, le bilan toxicologique était négatif et aucun facteur déclenchant sous-jacent clair n'avait été identifié. Une hémodialyse d'urgence a été organisée afin d'atténuer l'acidose. Diagnostic: Le patient a reçu un traitement initial de dialyze pendant quatre heures; les analyses de laboratoire post-hémodialyse ont montré une amélioration significative de l'acidose, du taux de lactate sérique et de l'état clinique (cognition, hypothermie). Vu cette résolution rapide, un échantillon de sang prédialyse a été envoyé pour analyze du taux de metformine plasmatique; cette analyze a révélé un taux significativement élevé de 60 µg/ml (plage thérapeutique: 1-2 µg/ml). Interventions et résultats: Au cours d'une vérification minutieuse de la médication du patient à l'unité de dialyze, ce dernier a déclaré n'avoir jamais entendu parler de metformine; il n'y avait par ailleurs aucune trace d'ordonnance remplie à sa pharmacie pour ce médicament. Étant donné son mode de vie en logements partagés, on a présumé que le patient avait pris des médicaments qui avaient été prescrits à un colocataire. Afin d'améliorer l'observance du traitement, plusieurs des autres médicaments du patient, notamment ses antihypertenseurs, ont par la suite été administrés les jours de dialyze, après la séance. Enseignements tirés: Maintenir un diagnostic différentiel large pour les patients présentant un syndrome clinique compatible avec une intoxication aiguë, et ce, même si aucun médicament responsable n'est identifiable à l'anamnèse, en particulier chez les patients qui ont des antécédents sociaux évocateurs.L'acidose métabolique à trou anionique (AMTA) est fréquente chez les patients hospitalisés, mais nécessite parfois une analyze plus approfondie des antécédents et/ou des tests de confirmations pour parvenir à déterminer la cause fondamentale sous-tendant les causes habituelles de l'AMTA comme l'acidose lactique ou l'acidocétose.Le principal traitement pour contrer la toxicité de la metformine est la réanimation et les traitements de soutien. Les propriétés biochimiques de la metformine la rendent cependant facilement dialysable par diffusion ou convection.Le groupe de travail EXTRIP (EXtracorporeal TReatments In Poisoning) recommande l'hémodialyse pour gérer la toxicité de la metformine en présence d'un taux de lactate sérique supérieur à 20 mmol/L, d'un pH sanguin inférieur à 7, d'un échec du traitement standard, de dommages aux organes cibles (insuffisance hépatique ou rénale) ou d'une altération de l'état de conscience.

Rh(D) immune globulin administration in pregnancy: Retrospective audit of patient safety events followed by targeted educational intervention with Bayesian analysis.

OBJECTIVES: To examine local patient safety events related to the administration of anti-Rh(D) immune globin (RhIG) during pregnancy, and to follow-up with targeted educational intervention to improve knowledge of this process. BACKGROUND: Administration RhIG is established treatment for the prevention of haemolytic disease of the foetus and newborn (HDFN). However, patient safety events in relation to its correct use continue to occur. METHODS: A retrospective audit of patient safety events related to RhIG administration during pregnancy was performed. Targeted educational intervention in the form of PowerPoint® presentation were given to nursing staff, laboratory staff and physicians and evaluated with pre- and post-tests using multiple-choice questions given immediately before and after the presentation. RESULTS: An annual incidence of 0.24% of patient safety events related to the administration of RhIG during pregnancy was found. These events were mostly in the preanalytical phase, for example mislabelled samples or samples for D-rosette/Kleihauer-Betke testing drawn from the baby, not the mother. Using Bayesian analysis, the probability of positive effect for the targeted educational intervention was 100% with a median improved score of 29%. This was compared with a control group using standard curriculum education intervention based on the current curriculum for nursing, laboratory and medical students which showed a median improved score of only 4.4%. CONCLUSIONS: Administration of RhIG during pregnancy is a multistep process involving health care professionals of several disciplines providing opportunities to enhance the curriculum for nursing, laboratory and medical students and to ensure on-going education.

Imputing missing laboratory results may return erroneous values because they are not missing at random.

BACKGROUND AND OBJECTIVES: Regression models incorporating laboratory tests treat unordered tests as missing and are often imputed. Imputation typically assumes that data are "missing at random" (MAR, test's order status is unrelated to its result after accounting for other variables). This study examined the validity of this assumption. METHODS: We included 14 biochemistry tests. All tests were measured regardless of test order status. Test-stratified multiple linear regression determined the independent association between test result and order status after adjusting for patient age, sex, comorbidities, and patient location. Testing likelihood models were created for all tests using hospital-wide data. RESULTS: Four hundred thirty-four patients were included (mean age [standard deviation] 60.7 [19.1], 50.5% female). In 9 of 14 tests (64.2%), test results were significantly associated with order status after adjustment. Results were significantly more abnormal when tests were ordered for 6 tests and significantly more normal for 3 tests. Test abnormality increased as testing likelihood decreased. CONCLUSIONS: These data suggest that laboratory data are often not MAR. The direction and extent of differences in missing laboratory test values varies between tests. Overall the abnormality of ordered tests increased as testing likelihood decreased. These results suggest that imputating missing laboratory data may return biased values.

Choosing which in-hospital laboratory tests to target for intervention: a scoping review.

INTRODUCTION: Some laboratory testing practices may be of low value, leading to wasted resources and potential patient harm. Our scoping review investigated factors and processes that developers report using to inform decisions about what tests to target for practice improvement. METHODS: We searched Medline on May 30th, 2019 and June 28th, 2021 and included guidelines, recommendation statements, or empirical studies related to test ordering practices. Studies were included if they were conducted in a tertiary care setting, reported making a choice about a specific test requiring intervention, and reported at least one factor informing that choice. We extracted descriptive details, tests chosen, processes used to make the choice, and factors guiding test choice. RESULTS: From 114 eligible studies, we identified 30 factors related to test choice including clinical value, cost, prevalence of test, quality of test, and actionability of test results. We identified nine different processes used to inform decisions regarding where to spend intervention resources. CONCLUSIONS: Intervention developers face difficult choices when deciding where to put scarce resources intended to improve test utilization. Factors and processes identified here can be used to inform a framework to help intervention developers make choices relevant to improving testing practices.

Comparison of Three Methods for LDLC Calculation for Cardiovascular Disease Risk Categorisation in Three Distinct Patient Populations.

BACKGROUND: Limitations of the Friedewald equation for low-density-lipoprotein cholesterol (F-LDLC) calculation led to the Martin-Hopkins (M-LDLC) and Sampson-National Institutes of Health (S-LDLC) equations. We studied these newer calculations of LDLC for correlation and discordance for stratification into the Canadian Cardiovascular Society (CCS) 2021 Dyslipidemia Guidelines' cardiovascular disease (CVD) risk categories. METHODS: We performed analyses on lipid profiles from 3 populations: records of a hospital biochemistry laboratory (population 1), lipid clinic patients without select monogenic dyslipidemias (population 2A), and lipid clinic patients with familial hypercholesterolemia (FH; population 2B). RESULTS: There was very strong correlation among the 3 calculated LDLC. In populations 1 and 2A, M-LDLC and S-LDLC were progressively higher than F-LDLC as triglyceride (TG) levels increased from normal to ∼ 5 mmol/L. In population 2B, M-LDLC was higher than F-LDLC, but S-LDLC was progressively lower than F-LDLC. Using the CCS 2021 guidelines' 4 CVD risk categories, 7.0% (population 2A) to 7.2% (population 1) of cases for M-LDLC vs F-LDLC and 3.9% (population 2A) to 4.4% (population 1) of cases for S-LDLC vs F-LDLC were reclassified to an adjacent CVD risk category, mostly from a lower to a higher risk category. CONCLUSIONS: Switching from F-LDLC to S-LDLC or M-LDLC can reclassify up to ∼ 4.4% or 7.2% of patients, respectively, to another CCS CVD risk category. The difference between F-LDLC and M-LDLC or S-LDLC is greater with higher TG, and with lower LDLC. We recommend that clinical laboratories switch to reporting results from either M-LDLC or S-LDLC, but S-LDLC should not be used in FH patients, pending further studies.

Relationship between Plasma Zinc and Red Blood Cell Zinc Levels in Hospitalized Patients.

BACKGROUND: Patient zinc stores are quantified with plasma or red blood cell (RBC) measures. The relationship between these 2 measures of zinc status has not been determined in a broad population of hospitalized patients. METHODS: Both plasma zinc and RBC zinc were prospectively collected and measured in 252 consenting patients admitted urgently to hospital. Plasma and RBC zinc levels were measured within 48 h of admission. We collected demographic, vitals, and laboratory data for use in multivariate regression models that included markers of acute disease severity and systemic inflammation. RESULTS: Plasma zinc and RBC zinc levels were low in 63% and 10% of hospitalized patients, respectively. Categorized zinc levels based on normal intervals for plasma and RBC zinc values were not related (χ2 0.47 [2 df] P = 0.79). The Pearson correlation coefficient between plasma zinc and RBC zinc was -0.09 (P = 0.15). After adjustments for multiple clinical covariates, the correlation coefficient remained insignificant (r = -0.11, P = 0.08). Plasma zinc was inversely associated with markers of inflammation including the neutrophil-to-lymphocyte ratio and temperature. CONCLUSIONS: Patient-specific plasma and RBC zinc are unrelated in hospitalized patients, possibly due to decreased values with acute illness seen in the former but not the latter. Future studies are required to determine which of these measures best predicts outcomes in hospitalized patients.

Does Diabetes Alter CSF Total Protein Levels? A Retrospective Cohort Study.

Background and Purpose: Elevation of total protein level in cerebrospinal fluid (CSF-TP) in diabetic patients is often disregarded by clinicians. However, existing studies on the topic have significant limitations, and therefore we aimed to explore the relationship between diabetes and CSF-TP in a large database of CSF samples. Methods: Retrospective review of all diagnostic lumbar punctures at the Ottawa Hospital between 1996-2016. Patients were excluded if they had elevated CSF cell counts, or a condition known to elevate CSF-TP. Multivariate linear regression modeling considered the effects of age, sex, and diabetes. Results: Among 6124 patients (746 with diabetes, 5378 without), mean CSF-TP did not differ significantly between groups (0.39 and 0.35 mmol/L, p = 0.2). When controlled for age and sex, there was no significant effect of diabetes on CSF-TP and no significant correlation between mean serum glucose and CSF-TP (R2 = 0.12). Conclusions: CSF-TP did not differ significantly between diabetic and non-diabetic groups, once the influence of age and sex was controlled. Elevated CSF-TP should be regarded as pathologic, even in the setting of diabetes.

Performance of the 2021 Race-Free CKD-EPI Creatinine- and Cystatin C-Based Estimated GFR Equations Among Kidney Transplant Recipients.

RATIONALE & OBJECTIVE: Race-free estimated glomerular filtration rate (eGFR) equations incorporating creatinine with and without cystatin C were recently developed and recommended for routine use. However, the performance of these equations among kidney transplant recipients (KTRs) remains unknown. STUDY DESIGN: Cross-sectional study to validate the 2021 race-free Chronic Kidney Disease (CKD) Epidemiology Collaboration (CKD-EPI) eGFR equation based on creatinine alone (eGFRcr) or based on creatinine and cystatin C (eGFRcr-cys) among KTRs. SETTING & PARTICIPANTS: KTRs in stable condition (N = 415) from Canada and New Zealand with same-day measurements of creatinine, cystatin C, and glomerular filtration rate (GFR) using radiolabeled diethylenetriaminepentaacetic acid. TESTS COMPARED: The 2009 CKD-EPI eGFRcr, 2021 CKD-EPI eGFRcr, 2012 CKD-EPI eGFRcr-cys, 2021 CKD-EPI eGFRcr-cys, 2012 CKD-EPI eGFRcys, and Modification of Diet in Renal Disease (MDRD) Study eGFR equations were compared with measured GFR. OUTCOMES: Bias, precision, accuracy, and correct classification by CKD stage. Bias was defined as the difference between estimated and measured GFR. Precision was represented by the interquartile range. Accuracy was defined as the percentages of participants with eGFRs within 10%/20%/30% (P10/P20/P30) of measured GFR, root mean square error, and mean absolute error. RESULTS: 87% of patients studied were White, 3% Black, and 10% other races. Mean measured GFR was 53 ± 19 (SD) mL/min/1.73 m2. The 2009 and 2021 CKD-EPI eGFRcr equations demonstrated similar median bias (-2.3 vs -0.2 mL/min/1.73 m2, respectively), precision (14.5 vs 14.9 mL/min/1.73 m2), and accuracy (P10/P20/P30, 32%/65%/84% vs 33%/63%/84%). The 2012 and 2021 CKD-EPI eGFRcr-cys equations also demonstrated similar median bias (-3.6 vs 0.3 mL/min/1.73 m2, respectively), precision (13.3 vs 14.3 mL/min/1.73 m2), and accuracy (P10/P20/P30, 32%/63%/80% vs 32%/67%/83%). No clear difference in performance was detected between the 2021 CKD-EPI eGFRcr and eGFRcr-cys equations among KTRs. The proportion of correct classification by CKD stage was similar across all eGFR equations. LIMITATIONS: Moderate sample size, few patients had a GFR <30 mL/min/1.73 m2, and the large majority of patients were White. CONCLUSIONS: Among KTRs, the 2021 race-free CKD-EPI eGFR equations perform similarly to the previous CKD-EPI equations that included race correction terms. No significant difference in performance was observed between the 2021 CKD-EPI eGFRcr and eGFRcr-cys equations in the kidney transplant population.

Transformation of Sequential Hospital and Outpatient Laboratory Data into Between-Day Reference Change Values.

BACKGROUND: Serial differences between intrapatient consecutive measurements can be transformed into Taylor series of variation vs time with the intersection at time = 0 (y0) equal to the total variation (analytical + biological + preanalytical). With small preanalytical variation, y0, expressed as a percentage of the mean, is equal to the variable component of the reference change value (RCV) calculation: (CVA2 + CVI2)1/2. METHODS: We determined the between-day RCV of patient data for 17 analytes and compared them to healthy participants' RCVs. We analyzed 653 consecutive days of Dartmouth-Hitchcock Roche Modular general chemistry data (4.2 million results: 60% inpatient, 40% outpatient). The serial patient values of 17 analytes were transformed into 95% 2-sided RCV (RCVAlternate), and 3 sets of RCVhealthy were calculated from 3 Roche Modular analyzers' quality control summaries and CVI derived from biological variation (BV) studies using healthy participants. RESULTS: The RCVAlternate values are similar to RCVhealthy derived from known components of variation. For sodium, chloride, bicarbonate calcium, magnesium, phosphate, alanine aminotransferase, albumin, and total protein, the RCVs are equivalent. As expected, increased variation was found for glucose, aspartate aminotransferase, creatinine, and potassium. Direct bilirubin and urea demonstrated lower variation. CONCLUSIONS: Our RCVAlternate values integrate known and unknown components of analytic, biologic, and preanalytic variation, and depict the variations observed by clinical teams that make medical decisions based on the test values. The RCVAlternate values are similar to the RCVhealthy values derived from known components of variation and suggest further studies to better understand the results being generated on actual patients tested in typical laboratory environments.

Laboratory Detection and Initial Diagnosis of Monoclonal Gammopathies.

CONTEXT.­: The process for identifying patients with monoclonal gammopathies is complex. Initial detection of a monoclonal immunoglobulin protein (M protein) in the serum or urine often requires compilation of analytical data from several areas of the laboratory. The detection of M proteins depends on adequacy of the sample provided, available clinical information, and the laboratory tests used. OBJECTIVE.­: To develop an evidence-based guideline for the initial laboratory detection of M proteins. DESIGN.­: To develop evidence-based recommendations, the College of American Pathologists convened a panel of experts in the diagnosis and treatment of monoclonal gammopathies and the laboratory procedures used for the initial detection of M proteins. The panel conducted a systematic literature review to address key questions. Using the Grading of Recommendations Assessment, Development, and Evaluation approach, recommendations were created based on the available evidence, strength of that evidence, and key judgements as defined in the Grading of Recommendations Assessment, Development, and Evaluation Evidence to Decision framework. RESULTS.­: Nine guideline statements were established to optimize sample selection and testing for the initial detection and quantitative measurement of M proteins used to diagnose monoclonal gammopathies. CONCLUSIONS.­: This guideline was constructed to harmonize and strengthen the initial detection of an M protein in patients displaying symptoms or laboratory features of a monoclonal gammopathy. It endorses more comprehensive initial testing when there is suspicion of amyloid light chain amyloidosis or neuropathies, such as POEMS (polyneuropathy, organomegaly, endocrinopathy, M protein, and skin changes) syndrome, associated with an M protein.

Limited Evidence for Use of a Black Race Modifier in eGFR Calculations: A Systematic Review.

BACKGROUND: Commonly used estimated glomerular filtration rate (eGFR) equations include a Black race modifier (BRM) that was incorporated during equation derivation. Race is a social construct, and a poorly characterized variable that is applied inconsistently in clinical settings. The BRM results in higher eGFR for any creatinine concentration, implying fundamental differences in creatinine production or excretion in Black individuals compared to other populations. Equations without inclusion of the BRM have the potential to detect kidney disease earlier in patients at the greatest risk of chronic kidney disease (CKD), but also has the potential to over-diagnose CKD or impact downstream clinical interventions. The purpose of this study was to use an evidence-based approach to systematically evaluate the literature relevant to the performance of the eGFR equations with and without the BRM and to examine the clinical impact of the use or removal. CONTENT: PubMed and Embase databases were searched for studies comparing measured GFR to eGFR in racially diverse adult populations using the Modification of Diet in Renal Disease or the 2009-Chronic Kidney Disease Epidemiology Collaboration-creatinine equations based on standardized creatinine measurements. Additionally, we searched for studies comparing clinical use of eGFR calculated with and without the BRM. Here, 8632 unique publications were identified; an additional 3 studies were added post hoc. In total, 96 studies were subjected to further analysis and 44 studies were used to make a final assessment. SUMMARY: There is limited published evidence to support the use of a BRM in eGFR equations.

Reproducible manuscript preparation with RMarkdown application to JMSACL and other Elsevier Journals.

INTRODUCTION: With the rising complexity of modern multimarker analytical techniques and notable scientific publication retractions required for erroneous statistical analysis, there is increasing awareness of the importance of research transparency and reproducibility. The development of mature open-source tools for literate programming in multiple langauge paradigms has made fully-reproducible authorship possible. OBJECTIVES: We describe the procedure for manuscript preparation using RMarkdown and the R statistical programming language with application to JMSACL or any other Elsevier journal. METHODS: An instructional manuscript has been prepared in the RMarkdown markup language with stepwise directions on preparing sections, subsections, lists, tables, figures and reference management in an entirely reproducible format. RESULTS: From RMarkdown code, a submission-ready PDF is generated and JMSACL-compatible LaTeX code is generated. These can be uploaded to the Editorial Manager. CONCLUSION: A completely reproducible manuscript preparation pipeline using the R and RMarkdown is described.

Continuous reference intervals for pediatric testosterone, sex hormone binding globulin and free testosterone using quantile regression.

Testosterone (T), sex hormone binding globulin (SHBG), free testosterone (FT) and bioavailable testosterone (BAT) are commonly employed tests in pediatric endocrinology and all require age-dependent reference intervals for interpretation. The common methods used to derive these reference intervals require decisions about data shape and/or age partition thresholds, which can result in sharp differences between age groups, particularly for pubescent children. Partitioning also results in a form of data loss, where data from one age-bin is completed disconnected from the adjacent age-bins. Non-parametric continuous reference intervals methods have previously been developed to avoid some of these drawbacks. These strategies use all the available data and smooth transitions between ages avoiding partitioning. However, the fitting process involves selection and adjustment of many parameters and it can be difficult to maintain a reproducible approach. Here we provide a workflow for non-parametric continuous reference intervals applied to T, FT, BAT, and SHBG using the R language quantregGrowth package. T measurements were determined by LC-MS/MS, FT and BAT were calculated, and SHBG was measured on the Roche Cobas e601. The continuous interval methodology is described in detail with code examples and illustrations for reproducibility.

Rise of the Machines: Artificial Intelligence and the Clinical Laboratory.

BACKGROUND: Artificial intelligence (AI) is rapidly being developed and implemented to augment and automate decision-making across healthcare systems. Being an essential part of these systems, laboratories will see significant growth in AI applications for the foreseeable future. CONTENT: In laboratory medicine, AI can be used for operational decision-making and automating or augmenting human-based workflows. Specific applications include instrument automation, error detection, forecasting, result interpretation, test utilization, genomics, and image analysis. If not doing so today, clinical laboratories will be using AI routinely in the future, therefore, laboratory experts should understand their potential role in this new area and the opportunities for AI technologies. The roles of laboratorians range from passive provision of data to fuel algorithms to developing entirely new algorithms, with subject matter expertise as a perfect fit in the middle. The technical development of algorithms is only a part of the overall picture, where the type, availability, and quality of data are at least as important. Implementation of AI algorithms also offers technical and usability challenges that need to be understood to be successful. Finally, as AI algorithms continue to become available, it is important to understand how to evaluate their validity and utility in the real world. SUMMARY: This review provides an overview of what AI is, examples of how it is currently being used in laboratory medicine, different ways for laboratorians to get involved in algorithm development, and key considerations for AI algorithm implementation and critical evaluation.