Risk Factors for Colistin Resistance among Gram-Negative Rods and Klebsiella pneumoniae Isolates.

Infections due to colistin-resistant (Colr) Gram-negative rods (GNRs) and colistin-resistant Klebsiella pneumoniae isolates in particular result in high associated mortality and poor treatment options. To determine the risk factors for recovery on culture of Colr GNRs and ColrK. pneumoniae, analyses were chosen to aid decisions at two separate time points: the first when only Gram stain results are available without any bacterial species information (corresponding to the Colr GNR model) and the second when organism identification is performed but prior to reporting of antimicrobial susceptibility testing results (corresponding to the ColrK. pneumoniae model). Cases were retrospectively analyzed at a major academic hospital system from 2011 to 2016. After excluding bacteria that were intrinsically resistant to colistin, a total of 28,512 GNR isolates (4,557 K. pneumoniae isolates) were analyzed, 128 of which were Colr (i.e., MIC > 2 µg/ml), including 68 of which that were ColrK. pneumoniae In multivariate analysis, risk factors for Colr GNRs were neurologic disease, residence in a skilled nursing facility prior to admission, receipt of carbapenems in the last 90 days, prior infection with a carbapenem-resistant organism, and receipt of ventilatory support (c-statistic = 0.81). Risk factors for ColrK. pneumoniae specifically were neurologic disease, residence in a skilled nursing facility prior to admission, receipt of carbapenems in the last 90 days, receipt of an anti-methicillin-resistant Staphylococcus aureus antimicrobial in the last 90 days, and prior infection with a carbapenem-resistant organism (c-statistic = 0.89). A scoring system derived from these models can be applied by providers to guide empirical antimicrobial therapy in patients with infections with suspected Colr GNR and ColrK. pneumoniae isolates.

Risk Factors for Development of Carbapenem Resistance Among Gram-Negative Rods.

BACKGROUND: Infections due to carbapenem-resistant Gram-negative rods (CR-GNR) are increasing in frequency and result in high morbidity and mortality. Appropriate initial antibiotic therapy is necessary to reduce adverse consequences and shorten length of stay. METHODS: To determine risk factors for recovery on culture of CR-GNR, cases were retrospectively analyzed at a major academic hospital system from 2011 to 2016. Ertapenem resistance (ER-GNR) and antipseudomonal (nonertapenem) carbapenem resistance (ACR-GNR) patterns were analyzed separately. A total of 30951 GNR isolates from 12370 patients were analyzed, 563 of which were ER and 1307 of which were ACR. RESULTS: In multivariate analysis, risk factors for ER-GNR were renal disease, admission from another health care facility, ventilation at any point before culture during the index hospitalization, receipt of any carbapenem in the prior 30 days, and receipt of any anti-methicillin-resistant Staphylococcus aureus (anti-MRSA) agent in the prior 30 days (c-statistic, 0.74). Risk factors for ACR-GNR were male sex, admission from another health care facility, ventilation at any point before culture during the index hospitalization, receipt of any carbapenem in the prior 30 days, and receipt of any anti-MRSA agent in the prior 30 days (c-statistic, 0.76). CONCLUSIONS: A straightforward scoring system derived from these models can be applied by providers to guide empiric antimicrobial therapy; it outperformed use of a standard hospital antibiogram in predicting infections with ER-GNR and ACR-GNR.

Risk factors for development of aminoglycoside resistance among gram-negative rods.

PURPOSE: Development of scoring systems to predict the risk of aminoglycoside resistance and to guide therapy is described. METHODS: Infections due to aminoglycoside-resistant gram-negative rods (AR-GNRs) are increasingly common and associated with adverse outcomes; selection of effective initial antibiotic therapy is necessary to reduce adverse consequences and shorten length of stay. To determine risk factors for AR-GNR recovery from culture, cases of GNR infection among patients admitted to 2 institutions in a major academic hospital system during the period 2011-2016 were retrospectively analyzed. Gentamicin and tobramycin resistance (GTR-GNR) and amikacin resistance (AmR-GNR) patterns were analyzed separately. A total of 26,154 GNR isolates from 12,516 patients were analyzed, 6,699 of which were GTR, and 2,467 of which were AmR. RESULTS: In multivariate analysis, risk factors for GTR-GNR were presence of weight loss, admission from another medical or long-term care facility, a hemoglobin level of <11 g/dL, receipt of any carbapenem in the prior 30 days, and receipt of any fluoroquinolone in the prior 30 days (C statistic, 0.63). Risk factors for AmR-GNR were diagnosis of cystic fibrosis, male gender, admission from another medical or long-term care facility, ventilation at any point prior to culture during the index hospitalization, receipt of any carbapenem in the prior 30 days, and receipt of any anti-MRSA agent in the prior 30 days (C statistic, 0.74). Multinomial and ordinal models demonstrated that the risk factors for the 2 resistance patterns differed significantly. CONCLUSION: A scoring system derived from the developed risk prediction models can be applied by providers to guide empirical antimicrobial therapy for treatment of GNR infections.

A Simple Derived Prediction Score for the Identification of an Elevated Pulmonary Artery Wedge Pressure Using Precatheterization Clinical Data in Patients Referred to a Pulmonary Hypertension Center.

BACKGROUND: One of the foremost diagnostic challenges in clinical pulmonary hypertension is discriminating between pulmonary arterial hypertension (group 1) and heart failure with preserved ejection fraction (group 2.2). Group 2.2 is defined as a normal left ventricular ejection fraction (> 50%) and a pulmonary arterial wedge pressure (PAWP) > 15 mm Hg. We aimed to determine whether patient history, demographics, and noninvasive measures could predict PAWP before to right heart catheterization. METHODS: Data were prospectively collected on 350 consecutive patients at a single tertiary care medical center; of these patients, 151 met criteria for entry into our study (88 in group 1 and 63 in group 2.2). Data included historical features, demographics, and results of a transthoracic echocardiogram. A multivariate regression model was developed to predict PAWP > 15 mm Hg. RESULTS: Univariate predictors of PAWP > 15 mm Hg included older age, higher BMI and weight, systemic systolic BP and pulse pressure, more features of the metabolic syndrome, presence of hypertension and left atrial enlargement, absence of right ventricular enlargement, and lower glomerular filtration rate and 6-min walk distance. The optimal model for predicting PAWP > 15 mm Hg was composed of age (> 68 years), BMI (> 30 kg/m(2)), absence of right ventricular enlargement, and presence of left atrial enlargement (area under the curve, 0.779). CONCLUSIONS: Clinical characteristics obtained before diagnostic right heart catheterization accurately predict the probability of elevation of PAWP > 15 mm Hg in patients with preserved ejection fraction. These combined clinical characteristics can be used a priori to predict the likelihood of group 2.2 pulmonary hypertension.

A data-driven approach to optimized medication dosing: a focus on heparin.

PURPOSE: To demonstrate a novel method that utilizes retrospective data to develop statistically optimal dosing strategies for medications with sensitive therapeutic windows. We illustrate our approach on intravenous unfractionated heparin, a medication which typically considers only patient weight and is frequently misdosed. METHODS: We identified available clinical features which impact patient response to heparin and extracted 1,511 patients from the multi-parameter intelligent monitoring in intensive care II database which met our inclusion criteria. These were used to develop two multivariate logistic regressions, modeling sub- and supra-therapeutic activated partial thromboplastin time (aPTT) as a function of clinical features. We combined information from these models to estimate an initial heparin dose that would, on a per-patient basis, maximize the probability of a therapeutic aPTT within 4-8 h of the initial infusion. We tested our model's ability to classifying therapeutic outcomes on a withheld dataset and compared performance to a weight-alone alternative using volume under surface (VUS) (a multiclass version of AUC). RESULTS: We observed statistically significant associations between sub- and supra-therapeutic aPTT, race, ICU type, gender, heparin dose, age and Sequential Organ Failure Assessment scores with mean validation AUC of 0.78 and 0.79 respectively. Our final model improved outcome classification over the weight-alone alternative, with VUS values of 0.48 vs. 0.42. CONCLUSIONS: This work represents an important step in the secondary use of health data in developing models to optimize drug dosing. The next step would be evaluating whether this approach indeed achieves target aPTT more reliably than the current weight-based heparin dosing in a randomized controlled trial.