Early machine learning prediction of hospitalized patients at low risk of respiratory deterioration or mortality in community-acquired pneumonia: Derivation and validation of a multivariable model

Current prognostic tools for pneumonia predominantly focus on mortality, often neglecting other crucial outcomes such as the need for advanced respiratory support. The objective of this study was to develop and validate a tool that predicts the early risk of non-occurrence of respiratory deterioration or mortality. We conducted a single-center, retrospective cohort study involving hospitalized adult patients with community-acquired pneumonia (CAP) and acute hypoxic respiratory failure from January 2009 to December 2019 (n = 4379). We employed the gradient boosting machine (GBM) learning to create a model that estimates the likelihood of patients requiring advanced respiratory support (high flow nasal cannula [HFNC], non-invasive mechanical ventilation [NIMV], and invasive mechanical ventilation [IMV]) or facing mortality during hospitalization. This model utilized readily available data including demographic, physiologic, and laboratory data, sourced from electronic health records and obtained within the first six hours of admission. Out of the cohort, 890 patients (25.2%) either required advanced respiratory support or died during their hospital stay. Our predictive model displayed superior discrimination and higher sensitivity (cross-validation C-statistic = 0.71; specificity = 0.56; sensitivity = 0.72) compared to the pneumonia severity index (PSI) (C-statistic = 0.65; specificity = 0.91; sensitivity = 0.24; P value < 0.001), while maintaining a negative predictive value (NPV) of approximately 0.85. These data demonstrate that our machine learning model predicted the non-occurrence of respiratory deterioration or mortality among hospitalized CAP patients more accurately than the PSI. The enhanced sensitivity of this model holds potential for reliably excluding low-risk patients from pneumonia clinical trials

PRN OPINION PAPER: Application of precision medicine across pharmacy specialty areas

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149551/1/jac51107_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149551/2/jac51107.pd

Controversies in acute kidney injury: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) conference

In 2012, Kidney Disease: Improving Global Outcomes (KDIGO) published a guideline on the classification and management of acute kidney injury (AKI). The guideline was derived from evidence available through February 2011. Since then, new evidence has emerged that has important implications for clinical practice in diagnosing and managing AKI. In April of 2019, KDIGO held a controversies conference entitled Acute Kidney Injury with the following goals: determine best practices and areas of uncertainty in treating AKI; review key relevant literature published since the 2012 KDIGO AKI guideline; address ongoing controversial issues; identify new topics or issues to be revisited for the next iteration of the KDIGO AKI guideline; and outline research needed to improve AKI management. Here, we present the findings of this conference and describe key areas that future guidelines may address

Cystatin C: A Primer for Pharmacists

Pharmacists are at the forefront of dosing and monitoring medications eliminated by or toxic to the kidney. To evaluate the effectiveness and safety of these medications, accurate measurement of kidney function is paramount. The mainstay of kidney assessment for drug dosing and monitoring is serum creatinine (SCr)-based estimation equations. Yet, SCr has known limitations including its insensitivity to underlying changes in kidney function and the numerous non-kidney factors that are incompletely accounted for in equations to estimate glomerular filtration rate (eGFR). Serum cystatin C (cysC) is a biomarker that can serve as an adjunct or alternative to SCr to evaluate kidney function for drug dosing. Pharmacists must be educated about the strengths and limitations of cysC prior to applying it to medication management. Not all patient populations have been studied and some evaluations demonstrated large variations in the relationship between cysC and GFR. Use of eGFR equations incorporating cysC should be reserved for drug management in scenarios with demonstrated outcomes, including to improve pharmacodynamic target attainment for antibiotics or reduce drug toxicity. This article provides an overview of cysC, discusses evidence around its use in medication dosing and in special populations, and describes practical considerations for application and implementation

Renal Recovery following Liposomal Amphotericin B-Induced Nephrotoxicity

Background. Acute kidney injury (AKI) is a common complication of treatment with liposomal amphotericin B (LAmB). The trajectory of renal recovery after LAmB-associated AKI has not been well described, nor has effect of LAmB dose on recovery of renal function been explored. Objective. Characterize the pattern of renal recovery after incident AKI during LAmB and determine potential influencing factors. Methods. This retrospective cohort study analyzed patients who developed a ≥50% increase in serum creatinine while on LAmB. Patients were followed up until complete renal recovery or death or for 30 days, whichever occurred first. The primary outcome was complete renal recovery, defined as serum creatinine convalescence to within 10% of the patient’s pretreatment baseline. Multivariable modeling was used to identify independent predictors of renal recovery. Results. Ninety-eight patients experienced nephrotoxicity during LAmB, 94% of which received doses <7 mg/kg/day. Fifty-one patients at least partially recovered renal function and, of these, 32 exhibited complete recovery after a mean 9.8 ± 7.8 days. No statistical relationship was found between LAmB dose at the time of AKI or cumulative exposure to LAmB and the likelihood of renal recovery. Concomitant nephrotoxins, age, and pretreatment renal function did not modify this effect in multivariable analysis. Conclusion and Relevance. Our data suggests that LAmB dose did not impact the likelihood of renal recovery. Additional investigation is needed to confirm these findings when aggressive dosing strategies are employe. Additional research is also warranted to further characterize the course of recovery after LAmB-associated nephrotoxicity and comprehensive spectrum of renal outcomes

Medications with high node degrees.

Medications with high node degrees.</p

Top 20 significant AKI-correlated medications for two subgroups.

Top 20 significant AKI-correlated medications for two subgroups.</p

Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosing

Abstract Background Artificial intelligence (AI) tools are more effective if accepted by clinicians. We developed an AI-based clinical decision support system (CDSS) to facilitate vancomycin dosing. This qualitative study assesses clinicians' perceptions regarding CDSS implementation. Methods Thirteen semi-structured interviews were conducted with critical care pharmacists, at Mayo Clinic (Rochester, MN), from March through April 2020. Eight clinical cases were discussed with each pharmacist (N = 104). Following initial responses, we revealed the CDSS recommendations to assess participants' reactions and feedback. Interviews were audio-recorded, transcribed, and summarized. Results The participants reported considerable time and effort invested daily in individualizing vancomycin therapy for hospitalized patients. Most pharmacists agreed that such a CDSS could favorably affect (N = 8, 62%) or enhance (9, 69%) their ability to make vancomycin dosing decisions. In case-based evaluations, pharmacists' empiric doses differed from the CDSS recommendation in most cases (88/104, 85%). Following revealing the CDSS recommendations, we noted 78% (69/88) discrepant doses. In discrepant cases, pharmacists indicated they would not alter their recommendations. The reasons for declining the CDSS recommendation were general distrust of CDSS, lack of dynamic evaluation and in-depth analysis, inability to integrate all clinical data, and lack of a risk index. Conclusion While pharmacists acknowledged enthusiasm about the advantages of AI-based models to improve drug dosing, they were reluctant to integrate the tool into clinical practice. Additional research is necessary to determine the optimal approach to implementing CDSS at the point of care acceptable to clinicians and effective at improving patient outcomes

Development and performance of a novel vasopressor-driven mortality prediction model in septic shock

Abstract Background Vasoactive medications are essential in septic shock, but are not fully incorporated into current mortality prediction risk scores. We sought to develop a novel mortality prediction model for septic shock incorporating quantitative vasoactive medication usage. Methods Quantitative vasopressor use was calculated in a cohort of 5352 septic shock patients and compared using norepinephrine equivalents (NEE), cumulative vasopressor index and the vasoactive inotrope score models. Having best discrimination prediction, log10NEE was selected for further development of a novel prediction model for 28-day and 1-year mortality via backward stepwise logistic regression. This model termed ‘MAVIC’ (Mechanical ventilation, Acute Physiology And Chronic Health Evaluation-III, Vasopressors, Inotropes, Charlson comorbidity index) was then compared to Acute Physiology And Chronic Health Evaluation-III (APACHE-III) and Sequential Organ Failure Assessment (SOFA) scores in an independent validation cohort for its accuracy in predicting 28-day and 1-year mortality. Measurements and main results The MAVIC model was superior to the APACHE-III and SOFA scores in its ability to predict 28-day mortality (area under receiver operating characteristic curve [AUROC] 0.73 vs. 0.66 and 0.60) and 1-year mortality (AUROC 0.74 vs. 0.66 and 0.60), respectively. Conclusions The incorporation of quantitative vasopressor usage into a novel ‘MAVIC’ model results in superior 28-day and 1-year mortality risk prediction in a large cohort of patients with septic shock