Abstract
Background
Altered pharmacokinetics in obese patients raise concerns over worse clinical outcomes. This study assessed whether obese patients receiving a β-lactam have worse clinical outcomes compared to nonobese patients and to identify if therapeutic drug monitoring may be beneficial.
Methods
This multicenter, retrospective cohort included hospitalized adults admitted from July 2015 to July 2017 treated with a β-lactam as definitive monotherapy against a gram-negative bacilli for ≥72 hours. Patients were excluded if there was lack of source control or if polymicrobial infections required >1 antibiotic for definitive therapy. Patients were classified based on body mass index (BMI): nonobese (BMI ≤29.9 kg/m2) and obese (BMI ≥30.0 kg/m2). The primary outcome was clinical treatment failure, and secondary outcomes were hospital length of stay, inpatient all-cause mortality, and 30-day all-cause readmission.
Results
There were 257 (43.6%) obese patients and 332 (56.4%) nonobese patients included. The most common infections were urinary (50.9%) and respiratory (31.4%). Definitive treatment was driven by third-generation cephalosporins (46.9%) and cefepime (44.7%). Treatment failure occurred in 131 (51%) obese patients and 109 (32.8%) nonobese patients (P < .001). Obesity and respiratory source were independently associated with increased likelihood of treatment failure. Obese patients were hospitalized longer than nonobese patients (P = .002), but no differences were found for all-cause mortality (P = .117) or infection-related readmission (0 = 0.112).
Conclusions
Obese patients treated with β-lactams have higher rates of treatment failure and longer hospitalization periods than nonobese patients. Future studies are needed to assess the impact of therapeutic drug monitoring and specific dosing recommendations for targeted infection types.
Keywords: β-lactams, cephalosporins, clinical failure, gram-negative infections, obesity
Altered pharmacokinetics in obese patients raise concerns over worse clinical outcomes. In this study, obese patients treated with β-lactams have higher rates of treatment failure and longer hospitalization periods than nonobese patients.
With the continued increase in obesity rates observed in the United States and worldwide, there has been a renewed interest in this population, particularly within the realm of infectious diseases and antimicrobial usage [1, 2]. Obese patients have higher risks of infections when compared to nonobese patients, primarily due to increased comorbidities, vascular complications, and slower wound healing. Altered pharmacokinetics, specifically increased volume of distribution and altered renal clearance, lead to concerns for worsened clinical outcomes in obese patients [3, 4]. Cefazolin underdosing for surgical site prophylaxis, particularly in obese patients, resulted in higher rates of postsurgical infections [5, 6]. This subsequently led to higher dosing recommendations based on patient weight within this population. Despite the risk of underdosing in the obese population, clinical outcomes data do not exist for most β-lactams.
One potential solution to the challenge of effectively treating obese patients with infections is therapeutic drug monitoring (TDM). TDM is routinely performed for certain antimicrobials, particularly those with narrow therapeutic windows or those that can be affected by variable pharmacokinetics or disease states [7, 8]. Historically, performance of β-lactam TDM has been infrequent. Therefore, the purpose of this study was to assess whether obese patients receiving a β-lactam have worsened clinical outcomes compared to nonobese patients and to identify if pursuing TDM may be beneficial in this patient population.
METHODS
This multicenter, retrospective cohort included hospitalized adult patients admitted from 1 July 2015 through 31 July 2017 who were treated with a β-lactam as definitive monotherapy for a gram-negative bacilli infection for at least 72 hours. Patients were excluded if source control was unable to be achieved within 72 hours and if polymicrobial infections were present that required >1 antibiotic for definitive therapy. If a patient had multiple occurrences of β-lactam monotherapy during the study period, only the first episode was included. Patients were classified into 2 groups based on body mass index (BMI): nonobese (BMI ≤29.9 kg/m2) and obese (BMI ≥30.0 kg/m2) [9]. The following data were extracted from the medical record: patient demographics, comorbidities, microbiological and antimicrobial treatment data, hospital length of stay, discharge disposition, hospital readmission within 30 days of discharge, and inpatient mortality within 30 days of administration of therapy. Sites of infection were categorized for definitive therapy based upon indication for the β-lactam, as well as notes within the medical record. The primary outcome was clinical treatment failure, defined as a composite of (1) change in definitive therapy >72 hours due to clinical worsening; (2) residual leukocytosis (white blood cell count >10 × 109 cells/L) >72 hours after treatment initiation; (3) presence of a fever (single temperature >38.3°C [100.9°F]) >72 hours after treatment initiation; or (4) readmission within 30 days due to reinfection with the same organism. Secondary outcomes were hospital length of stay, inpatient all-cause mortality, and 30-day all-cause readmission.
Categorical data were analyzed utilizing Pearson χ 2 or Fisher exact test; continuous data were analyzed utilizing the Mann-Whitney U test. A 2-sided P value of <.05 was considered statistically significant. A forward stepwise logistic regression analysis was conducted using the following variables: age, sex, race, obesity, serum creatinine, heart failure, chronic pulmonary disease, chronic kidney disease, dementia, cerebrovascular disease, complicated diabetes mellitus, cirrhosis, definitive antibiotic (first-, third-, or fourth-generation cephalosporins), definitive source (bloodstream, respiratory, urine), and causative organism. Variables that were significantly associated with treatment failure (P < .05) were included in the multivariable logistic regression model. A post hoc power analysis was conducted to determine if the effect size impacted the ability to detect a difference in the primary endpoint. The post hoc power was determined to be 99.4% with an α = .05 [10]. Statistical analyses were performed using SPSS (version 26.0, IBM, Armonk, New York). This study was approved by each respective institution’s institutional review board (IRB). All methods were carried out in accordance with relevant guidelines and regulations. The IRBs granted a waiver of consent as this was a retrospective study.
RESULTS
There were 589 patients included in the study with 257 (43.6%) patients in the obese group and 332 (56.4%) patients in the nonobese group. There were 194 (32.9%) patients included from site 1 and 395 (67.1%) included from site 2. The median weight and BMI in the obese group were 102.2 (interquartile range [IQR], 91.9–116.1) kg and 35.3 (IQR, 32.1–40.3) m/kg2, respectively. The median weight and BMI in the nonobese group were 68.1 (IQR, 59–80.1) kg and 24.4 (IQR, 21.4–27.2) m/kg2, respectively. When examining the total population, the median age was 70 (IQR, 59–80) years, and almost half (n = 255 [43.0%]) of the patients were male. Most patients were either African American (n = 312 [53.0%]) or white (n = 271 [46.0%]). There are several notable differences in baseline characteristics (Table 1), including age, renal function, and comorbid conditions. Obese patients were younger than nonobese patients (67 years vs 74 years; P = .004), yet they had poorer renal function as measured by their baseline serum creatinine (1.24 mg/dL vs 1.06 mg/dL; P = .004). Additionally, obese patients had a higher Charlson comorbidity score than nonobese patients (3 vs 2; P < .001), which was primarily driven by the presence of hypertension, congestive heart failure, chronic pulmonary disease, connective tissue disease, peptic ulcer disease, uncomplicated diabetes, and moderate-to-severe chronic kidney disease.
Table 1.
Baseline Patient Demographics
| Variable | Total | Obese | Nonobese | P Value |
|---|---|---|---|---|
| (N = 589) | (n = 257) | (n = 332) | ||
| Age, y, median (IQR) | 70 (59–80) | 67 (57.5–75) | 74 (61–83.8) | <.001 |
| Sex, male | 255 (43.4) | 94 (36.7) | 161 (48.6) | .004 |
| Race | ||||
| White | 271 (46) | 95 (37) | 176 (53) | <.001 |
| African American | 312 (53) | 161 (62.6) | 151 (45.5) | <.001 |
| Hispanic | 2 (0.3) | 1 (0.4) | 1 (0.3) | 1.000 |
| Other | 1 (0.2) | 0 (0) | 1 (0.3) | 1.000 |
| Unknown | 3 (0.5) | 0 (0) | 3 (0.9) | .261 |
| Serum creatinine, mg/dL, median (IQR) | 1.16 (0.80–1.96) | 1.24 (0.88–2.09) | 1.06 (0.78–1.79) | .004 |
| Weight, kg, median (IQR) | 83.1 (66.1–100.1) | 102.2 (91.9–116.1) | 68.1 (59–80.1) | <.001 |
| BMI, mg/kg2, median (IQR) | 28.5 (23.7–34.2) | 35.3 (32.1–40.3) | 24.4 (21.4–27.2) | <.001 |
| Comorbidities | ||||
| Hypertension | 498 (84.6) | 229 (89.1) | 269 (81) | .007 |
| History of myocardial infarction | 91 (15.4) | 25 (9.7) | 66 (19.9) | .001 |
| Congestive heart failure | 155 (26.3) | 88 (34.2) | 67 (20.2) | <.001 |
| Peripheral vascular disease | 67 (11.4) | 20 (7.8) | 47 (14.2) | .016 |
| Cerebrovascular disease | 174 (29.5) | 62 (24.1) | 112 (33.7) | .011 |
| Dementia | 68 (11.5) | 15 (5.8) | 53 (16) | <.001 |
| Chronic pulmonary disease | 185 (31.4) | 95 (37) | 90 (27.1) | .011 |
| Connective tissue disease | 69 (11.7) | 55 (21.4) | 14 (4.2) | <.001 |
| Peptic ulcer disease | 59 (10) | 44 (17.1) | 15 (4.5) | <.001 |
| Diabetes mellitus, uncomplicated | 115 (19.5) | 72 (28) | 43 (13) | <.001 |
| Diabetes mellitus, complicated | 97 (16.5) | 48 (18.7) | 49 (14.8) | .204 |
| CKD, moderate to severe | 132 (22.4) | 70 (27.2) | 62 (18.7) | .013 |
| Hemiplegia/paraplegia | 24 (4.1) | 11 (4.3) | 13 (3.9) | .824 |
| Leukemia | 9 (1.5) | 4 (1.6) | 5 (1.5) | 1.000 |
| Malignant lymphoma | 20 (3.4) | 7 (2.7) | 13 (3.9) | .428 |
| Solid tumor, not metastatic | 36 (6.1) | 24 (9.3) | 12 (3.6) | .004 |
| Solid tumor, metastatic | 23 (3.9) | 10 (3.9) | 13 (3.9) | .988 |
| Liver disease, mild | 12 (2) | 5 (1.9) | 7 (2.1) | .890 |
| Liver disease, moderate to severe | 13 (2.2) | 4 (1.6) | 9 (2.7) | .344 |
| AIDS | 2 (0.3) | 0 (0) | 2 (0.6) | .507 |
| Charlson score, median (IQR) | 3 (1–4) | 3 (2–5) | 2 (1–4) | <.001 |
Data are presented as No. (%) unless otherwise indicated.
Abbreviations: BMI, body mass index; CKD, chronic kidney disease; IQR, interquartile range.
Common empiric suspected sites of infection included urinary tract (55.5%), respiratory tract (34.3%), and bloodstream (27.8%), with no differences seen between groups. The most commonly administered empiric antibiotics included intravenous vancomycin (n = 243 [41.3%]), third-generation cephalosporins (n = 230 [39.0%]), cefepime (n = 214 [36.3%]), and β-lactam/β-lactamase inhibitors (n = 90 [15.3%]). There were significant differences in the use of empiric cefepime (30.0% obese vs 41.3% nonobese; P = .005) and β-lactam/β-lactamase inhibitors (19.5% obese vs 12.0% nonobese; P = .013). Additionally, nonobese patients were more likely to receive cefepime at 1 g every 6 hours compared to obese patients (33.6% vs 14.5%; P = .003). With regard to duration of empiric therapy, obese patients received third-generation cephalosporins for a longer duration (3 days vs 2 days; P < .001), while nonobese patients received cefepime for a longer duration (4 days vs 2 days; P < .001).
Cultures were commonly obtained from the urine (48.7%), blood (19.5%), and sputum (14.6%), with more urine cultures obtained in the nonobese patients (53.0% vs 43.2%; P = .018). A breakdown of the cultured organisms can be found in Table 2. There were minimal differences in organisms cultured between groups. The most common final diagnoses made were urinary tract infection (50.9%), respiratory tract infection (31.4%), and bloodstream infection (9.3%), with no differences in infection location between groups. Additionally, 39 (6.6%) patients had multiple sites of infections, but there was no difference between groups (8.6% obese vs 5.1% nonobese; P = .096). The minimum inhibitory concentrations (MICs) for the cultured organisms against first-generation cephalosporins were mostly ≤8 mg/L (n = 23 [48.9%]) or not available (n = 20 [42.6%]) with no differences between groups. Most MICs for third-generation cephalosporins were ≤1 mg/L (n = 178 [64.5%]) with more organisms cultured from nonobese patients having this MIC (70.3% vs 57.8%; P = .031). Most cultured organisms had a MIC ≤1 mg/L against cefepime (n = 77 [29.3%]) with no differences in MICs seen between groups.
Table 2.
Microbiological Characteristics
| Variable | Total | Obese | Nonobese | P Value |
|---|---|---|---|---|
| (N = 589) | (n = 257) | (n = 332) | ||
| Cultured organisms | ||||
| MRSA | 2 (0.3) | 2 (0.8) | 0 (0) | .190 |
| MSSA | 10 (1.7) | 3 (1.2) | 7 (2.1) | .525 |
| Staphylococcus spp | 5 (0.8) | 2 (0.8) | 3 (0.9) | 1.000 |
| Streptococcus spp | 16 (2.7) | 8 (3.1) | 8 (2.4) | .603 |
| Escherichia coli | 176 (29.9) | 75 (29.2) | 101 (30.4) | .745 |
| Enterobacter spp | 30 (5.1) | 14 (5.4) | 16 (4.8) | .731 |
| Citrobacter spp | 12 (2) | 10 (3.9) | 2 (0.6) | .005 |
| Klebsiella spp | 81 (13.8) | 36 (14) | 45 (13.6) | .874 |
| Proteus spp | 50 (8.5) | 22 (8.6) | 28 (8.4) | .956 |
| Pseudomonas aeruginosa | 58 (9.8) | 25 (9.7) | 33 (9.9) | .932 |
| Acinetobacter baumannii | 12 (2) | 6 (2.3) | 6 (1.8) | .653 |
| Other gram-negative aerobic organisms | 62 (10.5) | 30 (11.7) | 32 (9.6) | .425 |
| Anaerobic organisms | 8 (1.4) | 0 (0) | 8 (2.4) | .011 |
| Definitive infection location | ||||
| CNS | 4 (0.7) | 3 (1.2) | 1 (0.3) | .323 |
| Bloodstream | 55 (9.3) | 25 (9.7) | 30 (9) | .775 |
| Bone/joint | 8 (1.4) | 4 (1.6) | 4 (1.2) | .734 |
| Infective endocarditis | 2 (0.3) | 2 (0.8) | 0 (0) | .190 |
| Skin/wound | 35 (5.9) | 13 (5.1) | 22 (6.6) | .425 |
| Respiratory | 185 (31.4) | 81 (31.5) | 104 (31.3) | .960 |
| Intra-abdominal | 10 (1.7) | 6 (2.3) | 4 (1.2) | .345 |
| Urinary tract/gynecologic | 300 (50.9) | 136 (52.9) | 164 (49.4) | .397 |
| Multiple infection sites | 39 (6.6) | 22 (8.6) | 17 (5.1) | .096 |
| CNS + skin/wound | 2 (5.1) | 2 (9.2) | 0 (0) | .495 |
| Urinary tract/gynecologic + bloodstream | 15 (38.4) | 7 (31.8) | 8 (47) | .508 |
| Urinary tract/gynecologic + respiratory | 8 (20.5) | 5 (22.7) | 3 (17.6) | 1.000 |
| CNS + bloodstream | 1 (2.6) | 1 (4.5) | 0 (0) | 1.000 |
| Respiratory + bloodstream | 4 (10.2) | 3 (13.6) | 1 (5.9) | .618 |
| Respiratory + infective endocarditis | 1 (2.6) | 1 (4.5) | 0 (0) | 1.000 |
| Skin/wound + bone/joint | 1 (2.6) | 0 (0) | 1 (5.9) | .436 |
| Intra-abdominal + skin/wound | 1 (2.6) | 0 (0) | 1 (5.9) | .436 |
| Skin/wound + respiratory | 1 (2.6) | 0 (0) | 1 (5.9) | .436 |
| Bloodstream + bone/joint | 1 (2.6) | 0 (0) | 1 (5.9) | .436 |
| Bloodstream + urinary tract/gynecologic + intra-abdominal | 1 (2.6) | 1 (4.5) | 0 (0) | 1.000 |
| Bloodstream + urinary tract/gynecologic + respiratory | 3 (7.6) | 2 (9.2) | 1 (5.9) | 1.000 |
Data are presented as No. (%).
Abbreviations: CNS, central nervous system; MRSA, methicillin-resistant Staphylococcus aureus; MSSA, methicillin-susceptible Staphylococcus aureus.
A high percentage of patients (n = 276 [46.9%]) received a third-generation cephalosporin for definitive therapy, while 263 (44.7%) patients received cefepime, 47 (8.0%) patients received a first-generation cephalosporin, 2 (0.3%) received an anti-pseudomonal carbapenem, and 1 (0.2%) received a β-lactam/β-lactamase inhibitor combination. There were no differences in definitive doses for any β-lactam prescribed except for cefepime, with more patients in the obese group receiving 2 g every 8 hours (12.4% vs 5.3%; P = .041). A detailed breakdown of prescribed definitive β-lactam regimens is in Table 3. There was no difference between groups in duration of definitive therapy for any β-lactam.
Table 3.
Definitive β-Lactam Therapy Management
| Variable |
Total | Obese | Nonobese | P Value |
|---|---|---|---|---|
| (N = 589) | (n = 257) | (n = 332) | ||
| Definitive antibiotic selection | ||||
| β-lactam/β-lactamase inhibitors | 1 (0.2) | 1 (0.4) | 0 (0) | .436 |
| First-generation cephalosporins | 47 (8) | 14 (5.4) | 33 (9.9) | .046 |
| Third-generation cephalosporins | 276 (46.9) | 128 (49.8) | 148 (44.6) | .207 |
| Cefepime | 263 (44.7) | 113 (44) | 150 (45.2) | .769 |
| Anti-pseudomonal carbapenems | 2 (0.3) | 2 (0.8) | 0 (0) | .190 |
| Definitive drug regimens | ||||
| Piperacillin-tazobactam 3.375 g q6h | 1 (100) | 1 (100) | 0 (0) | |
| First-generation cephalosporins | ||||
| 500 mg q8h | 1 (2.1) | 0 (0) | 1 (3) | 1.000 |
| 1 g q8h | 27 (57.4) | 9 (64.3) | 18 (54.5) | .537 |
| 1 g q12h | 3 (6.4) | 0 (0) | 3 (9.1) | .544 |
| 1 g q24h | 1 (2.1) | 1 (7.1) | 0 (0) | .298 |
| 2 g q8h | 14 (29.8) | 4 (28.6) | 10 (30.3) | 1.000 |
| 2 g q12h | 1 (2.1) | 0 (0) | 1 (3) | 1.000 |
| Third-generation cephalosporins | ||||
| 0.5 g q24h | 1 (0.4) | 1 (0.8) | 0 (0) | .464 |
| 1 g q8h | 1 (0.4) | 1 (0.8) | 0 (0) | .464 |
| 1 g q12h | 3 (1.1) | 2 (1.6) | 1 (0.7) | .598 |
| 1 g q24h | 215 (77.9) | 94 (73.4) | 121 (81.8) | .097 |
| 2 g q12h | 2 (0.7) | 0 (0) | 2 (1.4) | .501 |
| 2 g q24h | 54 (19.6) | 30 (23.4) | 24 (16.2) | .132 |
| Cefepime | ||||
| 0.5 g q24h | 2 (0.8) | 0 (0) | 2 (1.3) | .508 |
| 1 g q6h | 72 (27.4) | 27 (23.9) | 45 (30) | .272 |
| 1 g q8h | 85 (32.3) | 37 (32.7) | 48 (32) | .898 |
| 1 g q12h | 37 (14.1) | 14 (12.4) | 23 (15.3) | .497 |
| 1 g q24h | 25 (9.5) | 14 (12.5) | 11 (7.3) | .166 |
| 2 g q8h | 22 (8.4) | 14 (12.4) | 8 (5.3) | .041 |
| 2 g q12h | 12 (4.6) | 2 (1.8) | 10 (6.7) | .060 |
| 2 g q24h | 8 (3) | 5 (4.4) | 3 (2) | .295 |
| Anti-pseudomonal carbapenems 1 g q6h | 2 (100) | 2 (100) | 0 (0) | |
| Duration of definitive therapy, days | ||||
| β-lactam/β-lactamase inhibitors | 2 days | 2 days | … | |
| First-generation cephalosporins, median (IQR) | 5 (4–7) | 5 (4–6) | 6 (4–7.5) | .364 |
| Third-generation cephalosporins, median (IQR) | 4 (3–6) | 4 (3–6) | 4 (3–6) | .169 |
| Cefepime, median (IQR) | 5 (4–7) | 5 (4–7) | 5 (4–7) | .216 |
| Anti-pseudomonal carbapenems | 4 days, 8 days | 4 days, 8 days | … |
Data are presented as No. (%) unless otherwise indicated.
Abbreviations: IQR, interquartile range; q6h, every 6 hours; q8h, every 8 hours; q12h, every 12 hours; q24h, every 24 hours.
Two hundred forty (40.7%) patients experienced treatment failure with 131 (51.0%) obese patients failing and 109 (32.8%) nonobese patients failing (P < .001; Table 4). A majority of patients failed due to unresolved leukocytosis (n = 181/240 [46.9%]); however, there were no differences between groups. Results of the logistic regression analysis are shown in Table 5. Only obesity (odds ratio, 2.3) and respiratory source (odds ratio, 3.1) were independently associated with treatment failure. There were no factors that were associated with reducing the odds of treatment failure.
Table 4.
Clinical Outcomes
| Variable |
Total | Obese | Nonobese | P Value |
|---|---|---|---|---|
| (N = 589) | (n = 257) | (n = 332) | ||
| Discharge disposition | ||||
| Died during hospitalization | 59 (10) | 27 (10.5) | 32 (9.6) | .728 |
| Home | 287 (48.7) | 130 (50.6) | 157 (47.3) | .428 |
| Skilled nursing/rehabilitation facility | 206 (35) | 85 (33.1) | 121 (36.4) | .395 |
| Hospice | 25 (4.2) | 9 (3.5) | 16 (4.8) | .432 |
| Another hospital | 8 (1.4) | 5 (1.9) | 3 (0.9) | .305 |
| Still admitted | 3 (0.5) | 1 (0.4) | 2 (0.6) | 1.000 |
| Prison | 1 (0.2) | 0 (0) | 1 (0.3) | 1.000 |
| Treatment failure | 240 (40.7) | 131 (51) | 109 (32.8) | <.001 |
| Readmission in 30 days due to reinfection | 50/240 (20.8) | 27/131 (20.6) | 23/109 (21.1) | .926 |
| Second antibiotic added | 61/240 (25.4) | 36/131 (27.5) | 25/109 (22.9) | .421 |
| Leukocytosis | 181/240 (75.4) | 101/131 (77.1) | 80/109 (73.4) | .507 |
| Fever | 53/240 (22.1) | 25/131 (19.1) | 28/109 (25.7) | .219 |
| 30-day inpatient all-cause mortality | 59/240 (24.6) | 27/131 (20.6) | 32/109 (29.4) | .117 |
| 30-day readmission | ||||
| No readmission | 449 (84.7) | 188 (81.7) | 261 (87) | .095 |
| Readmitted; infection-related | 50 (9.4) | 27 (11.7) | 23 (7.7) | .112 |
| Readmitted; non-infection-related | 31 (5.8) | 15 (6.5) | 16 (5.3) | .563 |
Data are presented as No. (%).
Table 5.
Risk Factors for Treatment Failure
| Factor | Odds Ratio (95% CI) | P Value |
|---|---|---|
| Obesity | 2.30 (1.57–3.35) | <.001 |
| Age | 1.00 (.98–1.01) | .431 |
| Female sex | 0.76 (.52–1.11) | .152 |
| Dementia | 0.96 (.52–1.79) | .908 |
| Enterobacter isolated | 2.06 (.88–4.82) | .096 |
| Citrobacter isolated | 2.97 (.59–14.94) | .187 |
| Pseudomonas isolated | 1.78 (.94–3.39) | .078 |
| Other gram-negative organism isolated | 1.61 (.88–2.95) | .121 |
| Definitive third-generation cephalosporin | 0.94 (.47–1.87) | .856 |
| Definitive fourth-generation cephalosporin | 1.12 (.55–2.29) | .747 |
| Respiratory source | 3.07 (1.87–5.04) | <.001 |
| Urinary-gynecologic source | 1.02 (.63–1.64) | .934 |
Abbreviation: CI, confidence interval.
The median hospital length of stay was 9 (IQR, 6–16) days; however, obese patients had a longer length of stay than nonobese patients (10 days vs 8 days; P = .002). Just under half the patients (n = 289 [48.7%]) were discharged home, with 206 (35%) patients discharged to a skilled nursing/rehabilitation facility, 25 (4.2%) patients discharged to hospice, 8 (1.4%) discharged to another hospital, 3 (0.5%) still admitted, and 1 (0.2%) discharged back to prison. Fifty-nine (10.0%) patients died during hospitalization. No differences were seen between groups. There were no additional deaths at 30 days postdischarge, and 449 (84.7%) patients were not readmitted within 30 days. Fifty (9.4%) patients were readmitted for an infectious cause (P = .095), and 31 (5.8%) were readmitted for a noninfectious cause (P = .563).
DISCUSSION
In this multicenter retrospective study, obese patients were more likely to experience clinical treatment failure when compared to nonobese patients; however, there was no clear driver of why treatment failure occurred more often in this population. Conversely, while obese patients experienced greater length of hospitalization, they had comparable rates of inpatient mortality.
Obese patients are more likely to have multiple comorbid conditions and tend to be younger when diagnosed [11]. Likewise, these obese patients were younger and had a higher disease burden than their nonobese counterparts. We anticipated that a population with greater comorbidities, coupled with the potential for underdosing of antibiotics, would experience greater rates of treatment failure. The most common cause of treatment failure in both groups was unresolved leukocytosis. One study of patients with ventilator-associated pneumonia evaluated the resolution of markers of infection and determined that leukocytes did not return to normal levels until 8 days later [12]. Height and weight were not captured on the patients in this study. However, early resolution of leukocytosis as an indicator of clinical failure may lead to overestimations of the endpoint. Obesity is associated with low-grade inflammation leading to higher baseline leukocyte counts [13]. Although not significant among those with treatment failure, there were significantly more obese patients with unresolved leukocytosis in the total population. This may have led to greater rates of second antibiotic prescriptions.
Obese patients stayed in the hospital on average 2 days longer than nonobese patients, but with no greater mortality. Obesity has been associated with greater length of hospital stay in general hospitalized patients and in sepsis [14, 15]. Mortality rates of obese patients with sepsis and pneumonia are reported to be lower than nonobese patients, which may explain why, despite experiencing higher rates of treatment failure and longer length of stay, there was no excess mortality in the obese group [15–18]. It is unclear whether those patients who experienced treatment failure contributed significantly to the excess length of hospitalization in the obese group. Likewise, since this study included all types of infections and a variety of β-lactam antibiotics, it is not statistically sound to draw conclusions based on specific infections or antibiotics that were associated with increased length of hospitalization or excess treatment failure. Future studies directed at individual antibiotics for specific infections will be necessary to evaluate these uncertainties.
There is limited evidence regarding dosing strategies of antimicrobials in obese patients. Physiologic changes associated with obesity may alter pharmacokinetic parameters such as volume of distribution and clearance, but these changes are not well defined for most antimicrobials and are challenging to predict. Cephalosporins are hydrophilic, so increases in volume of distribution would be less likely, as most agents would not freely distribute into excess adipose tissue [19]. In fact, studies with cefazolin for surgical prophylaxis have demonstrated that despite adequate serum concentrations in obese patients, tissue concentrations were inadequate [20, 21]. Increased doses of cefazolin and cefepime have been suggested to overcome changes in pharmacokinetics associated with obesity [22, 23]. Additionally, to achieve longer time of target concentration attainment, select β-lactam agents have been studied using prolonged-infusion dosing strategies [24, 25]. Improved clinical outcomes may be seen in critically ill patients utilizing prolonged infusion, but it is unknown whether this technique is beneficial for all patients [26, 27]. Both institutions utilized prolonged infusion in select patients during this study, but this was not standard practice during the time of our investigation. In our study of patients with mixed infections, it is unclear whether clinical outcomes and higher rates of treatment failure would be affected by modified dosing strategies in our population.
While recommendations for TDM are established for antimicrobials such as vancomycin and aminoglycosides to ensure safety and efficacy [28, 29], β-lactams have traditionally not required monitoring, given a wide safety profile. However, due to increasing antimicrobial resistance and pharmacokinetic variability, experts advocate for TDM of β-lactams to increase chances of achieving adequate serum concentrations in critically ill patients [23, 30, 31]. Although studies report variation in serum concentrations, TDM of β-lactams has not demonstrated greater rates of clinical success, although trials are ongoing [32]. Given altered β-lactam pharmacokinetics in obese patients and higher rates of treatment failure in our study group, TDM may be a reasonable option for this population; however, further studies are needed to determine its utility in clinical practice.
Limitations of this study include the retrospective study design. Specifically, the determination of primary treatment failure relied on documentation in electronic health records. It is possible patients had sources of leukocytosis and fever that were unrelated to the primary end point. Second, baseline characteristics, specifically comorbidities, were not similar among obese and nonobese patients. However, the Charlson comorbidity index was higher in obese patients; therefore, this population would be expected to have higher mortality compared to nonobese patients. We saw no significant difference in 30-day mortality between groups with similar dosing strategies. Finally, our study broadly looked at infections treated with β-lactam antibiotics, which limits the ability to draw conclusions regarding specific β-lactam dosing effectiveness.
CONCLUSIONS
This study demonstrates that obese patients admitted for infections and treated with β-lactam antibiotics have higher rates of treatment failure compared to patients who are not obese. Additionally, obese patients had greater length of hospital stay without greater mortality. Obese patients had greater disease burden, which hinders the ability to draw conclusions on the dosing of β-lactam antibiotics. Future studies are needed to determine specific dosing recommendations for targeted infection types, as well as using more accurate markers of treatment success, in order to adequately conclude that obesity contributes to infection-related treatment failure.
Notes
Author contributions. All authors were involved in data collection, data analysis, and manuscript composition.
Acknowledgments. The authors would like to acknowledge the contributions of Austin Morrison, PharmD; J. Taylor Loper, PharmD; Sarah Blackwell, PharmD, BCPS, BCCCP; Alyssa Chaudhari, PharmD; Kenda Germain, PharmD; and Megan Autrey, PharmD.
Potential conflicts of interest. K. E. B. has served on the speakers’ bureau for Paratek Pharmaceuticals. All other authors report no potential conflicts of interest.
All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
Presented in part: IDWeek, San Francisco, CA, 2–6 October 2018; American College of Clinical Pharmacy Global Conference on Clinical Pharmacy, Seattle, WA, 20–23 October 2018; American Society of Health-Systems Pharmacists Annual Meeting, Las Vegas, NG, 8–12 December 2019.
References
- 1.Ogden CL, Carroll MD, Fryar CD. Prevalence of overweight, obesity, and extreme obesity among adults: United States, trends 1960–1962 through 2013–2014. Natl Center Health Stat 2016; 6:1–6. [Google Scholar]
- 2.Flegal KM, Kruszon-Moran D, Carroll MD, et al. Trends in obesity among adults in the United States, 2005 to 2014. JAMA 2016; 315:2284–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Bauer LA. Chapter 3. Drug dosing in special populations: renal and hepatic disease, dialysis, heart failure, obesity, and drug interactions. In: Bauer LA, ed. Applied Clinical Pharmacokinetics. 2nd ed. New York: McGraw-Hill, 2014. [Google Scholar]
- 4.Hanley MJ, Abernethy DR, Greenblatt DJ. Effect of obesity on the pharmacokinetics of drugs in humans. Clin Pharmacokinet 2010; 49:71–87. [DOI] [PubMed] [Google Scholar]
- 5.Rondon AJ, Kheir MM, Tan TL, et al. Cefazolin prophylaxis for total joint arthroplasty: obese patients are frequently underdosed and at increased risk of periprosthetic joint infection. J Arthroplasty 2018; 33:3551–4. [DOI] [PubMed] [Google Scholar]
- 6.Morris AJ, Roberts SA, Grae N, Frampton CM. Surgical site infection rate is higher following hip and knee arthroplasty when cefazolin is underdosed. Am J Health Syst Pharm 2020; 77:434–40. [DOI] [PubMed] [Google Scholar]
- 7.Williams P, Cotta MO, Roberts JA. Pharmacokinetics/pharmacodynamics of β-lactams and therapeutic drug monitoring: from theory to practical issues in the intensive care unit. Semin Respir Crit Care Med 2019; 40:476–87. [DOI] [PubMed] [Google Scholar]
- 8.Muller AE, Huttner B, Huttner A. Therapeutic drug monitoring of beta-lactams and other antibiotics in the intensive care unit: which agents, which patients and which infections? Drugs 2018; 78:439–51. [DOI] [PubMed] [Google Scholar]
- 9.Centers for Disease Control and Prevention. Defining adult overweight and obesity.2021. Available at: https://www.cdc.gov/obesity/adult/defining.html. Accessed 30 April 2021.
- 10.ClinCalc.com. Post-hoc power calculator.2018. Available at: https://clincalc.com/stats/Power.aspx. Accessed 2 April 2021.
- 11.Mokdad AH, Ford ES, Bowman BA, et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 2003; 289:76–9. [DOI] [PubMed] [Google Scholar]
- 12.Dennesen PJ, van der Ven AJ, Kessels AG, et al. Resolution of infectious parameters after antimicrobial therapy in patients with ventilator-associated pneumonia. Am J Respir Crit Care Med 2001; 163:1371–5. [DOI] [PubMed] [Google Scholar]
- 13.Herishanu Y, Rogowski O, Polliack A, Marilus R. Leukocytosis in obese individuals: possible link in patients with unexplained persistent neutrophilia. Eur J Haematol 2006; 76:516–20. [DOI] [PubMed] [Google Scholar]
- 14.Zizza C, Herring AH, Stevens J, Popkin BM. Length of hospital stays among obese individuals. Am J Public Health 2004; 94:1587–91. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Nguyen AT, Tsai CL, Hwang LY, et al. Obesity and mortality, length of stay and hospital cost among patients with sepsis: a nationwide inpatient retrospective cohort study. PLoS One 2016; 11:e0154599. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Kahlon S, Eurich DT, Padwal RS, et al. Obesity and outcomes in patients hospitalized with pneumonia. Clin Microbiol Infect 2013; 19:709–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Atamna A, Elis A, Gilady E, et al. How obesity impacts outcomes of infectious diseases. Eur J Clin Microbiol Infect Dis 2017; 36:585–91. [DOI] [PubMed] [Google Scholar]
- 18.King P, Mortensen EM, Bollinger M, et al. Impact of obesity on outcomes for patients hospitalized with pneumonia. Eur Resp J 2013; 41:929–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Meng L, Mui E, Holubar MK, Deresinski SC. Comprehensive guidance for antibiotic dosing in obese adults. Pharmacotherapy 2017; 37:1415–31. [DOI] [PubMed] [Google Scholar]
- 20.Brill MJ, Houwink AP, Schmidt S, et al. Reduced subcutaneous tissue distribution of cefazolin in morbidly obese versus non-obese patients determined using clinical microdialysis. J Antimicrob Chemother 2014; 69:715–23. [DOI] [PubMed] [Google Scholar]
- 21.Kram JJF, Greer DM, Cabrera O, et al. Does current cefazolin dosing achieve adequate tissue and blood concentrations in obese women undergoing cesarean section? Eur J Obstet Gynecol Reprod Biol 2017; 210:334–41. [DOI] [PubMed] [Google Scholar]
- 22.Bratzler DW, Dellinger EP, Olsen KM, et al. Clinical practice guidelines for antimicrobial prophylaxis in surgery. Surg Infect (Larchmt) 2013; 14:73–156. [DOI] [PubMed] [Google Scholar]
- 23.Hites M, Taccone FS, Wolff F, et al. Case-control study of drug monitoring of β-lactams in obese critically ill patients. Antimicrob Agents Chemother 2013; 57:708–15. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Dow R, Rose W, Fox B, et al. Retrospective study of prolonged versus intermittent infusion piperacillin-tazobactam and meropenem in intensive care unit patients at an academic medical center. Infect Dis Clin Pract 2011; 19:413–7. [Google Scholar]
- 25.Roberts JA, Kirkpatrick CM, Roberts MS, et al. Meropenem dosing in critically ill patients with sepsis and without renal dysfunction: intermittent bolus versus continuous administration? Monte Carlo dosing simulations and subcutaneous tissue distribution. J Antimicrob Chemother 2009; 64:142–50. [DOI] [PubMed] [Google Scholar]
- 26.Lodise TP Jr, Lomaestro B, Drusano GL. Piperacillin-tazobactam for Pseudomonas aeruginosa infection: clinical implications of an extended-infusion dosing strategy. Clin Infect Dis 2007; 44:357–63. [DOI] [PubMed] [Google Scholar]
- 27.Abdul-Aziz MH, Sulaiman H, Mat-Nor MB, et al. Beta-Lactam Infusion in Severe Sepsis (BLISS): a prospective, two-centre, open-labelled randomised controlled trial of continuous versus intermittent beta-lactam infusion in critically ill patients with severe sepsis. Intensive Care Med 2016; 42:1535–45. [DOI] [PubMed] [Google Scholar]
- 28.Rybak MJ, Le J, Lodise TP, et al. Therapeutic monitoring of vancomycin for serious methicillin-resistant Staphylococcus aureus infections: a revised consensus guideline and review by the American Society of Health-System Pharmacists, the Infectious Diseases Society of America, the Pediatric Infectious Diseases Society, and the Society of Infectious Diseases Pharmacists. Am J Health Syst Pharm 2020; 77:835–64. [DOI] [PubMed] [Google Scholar]
- 29.Nicolau DP, Freeman CD, Belliveau PP, et al. Experience with a once-daily aminoglycoside program administered to 2,184 adult patients. Antimicrob Agents Chemother 1995; 39:650–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Economou CJP, Wong G, McWhinney B, et al. Impact of β-lactam antibiotic therapeutic drug monitoring on dose adjustments in critically ill patients undergoing continuous renal replacement therapy. Int J Antimicrob Agents 2017; 49:589–94. [DOI] [PubMed] [Google Scholar]
- 31.Wong G, Briscoe S, McWhinney B, et al. Therapeutic drug monitoring of β-lactam antibiotics in the critically ill: direct measurement of unbound drug concentrations to achieve appropriate drug exposures. J Antimicrob Chemother 2018; 73:3087–94. [DOI] [PubMed] [Google Scholar]
- 32.Abdulla A, Ewoldt TMJ, Hunfeld NGM, et al. The effect of therapeutic drug monitoring of beta-lactam and fluoroquinolones on clinical outcome in critically ill patients: the DOLPHIN trial protocol of a multi-centre randomised controlled trial. BMC Infect Dis 2020; 20:57. [DOI] [PMC free article] [PubMed] [Google Scholar]