Patient Heterogeneity and the J-Curve Relationship Between Time-to-Antibiotics and the Outcomes of Patients Admitted With Bacterial Infection.

OBJECTIVES: Sepsis remains a leading and preventable cause of hospital utilization and mortality in the United States. Despite updated guidelines, the optimal definition of sepsis as well as optimal timing of bundled treatment remain uncertain. Identifying patients with infection who benefit from early treatment is a necessary step for tailored interventions. In this study, we aimed to illustrate clinical predictors of time-to-antibiotics among patients with severe bacterial infection and model the effect of delay on risk-adjusted outcomes across different sepsis definitions. DESIGN: A multicenter retrospective observational study. SETTING: A seven-hospital network including academic tertiary care center. PATIENTS: Eighteen thousand three hundred fifteen patients admitted with severe bacterial illness with or without sepsis by either acute organ dysfunction (AOD) or systemic inflammatory response syndrome positivity. MEASUREMENTS AND MAIN RESULTS: The primary exposure was time to antibiotics. We identified patient predictors of time-to-antibiotics including demographics, chronic diagnoses, vitals, and laboratory results and determined the impact of delay on a composite of inhospital death or length of stay over 10 days. Distribution of time-to-antibiotics was similar across patients with and without sepsis. For all patients, a J-curve relationship between time-to-antibiotics and outcomes was observed, primarily driven by length of stay among patients without AOD. Patient characteristics provided good to excellent prediction of time-to-antibiotics irrespective of the presence of sepsis. Reduced time-to-antibiotics was associated with improved outcomes for all time points beyond 2.5 hours from presentation across sepsis definitions. CONCLUSIONS: Antibiotic timing is a function of patient factors regardless of sepsis criteria. Similarly, we show that early administration of antibiotics is associated with improved outcomes in all patients with severe bacterial illness. Our findings suggest identifying infection is a rate-limiting and actionable step that can improve outcomes in septic and nonseptic patients.

min-SIA: a Lightweight Algorithm to Predict the Risk of 6-Month Mortality at the Time of Hospital Admission.

BACKGROUND: Predicting death in a cohort of clinically diverse, multi-condition hospitalized patients is difficult. This frequently hinders timely serious illness care conversations. Prognostic models that can determine 6-month death risk at the time of hospital admission can improve access to serious illness care conversations. OBJECTIVE: The objective is to determine if the demographic, vital sign, and laboratory data from the first 48 h of a hospitalization can be used to accurately quantify 6-month mortality risk. DESIGN: This is a retrospective study using electronic medical record data linked with the state death registry. PARTICIPANTS: Participants were 158,323 hospitalized patients within a 6-hospital network over a 6-year period. MAIN MEASURES: Main measures are the following: the first set of vital signs, complete blood count, basic and complete metabolic panel, serum lactate, pro-BNP, troponin-I, INR, aPTT, demographic information, and associated ICD codes. The outcome of interest was death within 6 months. KEY RESULTS: Model performance was measured on the validation dataset. A random forest model-mini serious illness algorithm-used 8 variables from the initial 48 h of hospitalization and predicted death within 6 months with an AUC of 0.92 (0.91-0.93). Red cell distribution width was the most important prognostic variable. min-SIA (mini serious illness algorithm) was very well calibrated and estimated the probability of death to within 10% of the actual value. The discriminative ability of the min-SIA was significantly better than historical estimates of clinician performance. CONCLUSION: min-SIA algorithm can identify patients at high risk of 6-month mortality at the time of hospital admission. It can be used to improved access to timely, serious illness care conversations in high-risk patients.

Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study.

BACKGROUND: Predicting death in a cohort of clinically diverse, multicondition hospitalized patients is difficult. Prognostic models that use electronic medical record (EMR) data to determine 1-year death risk can improve end-of-life planning and risk adjustment for research. OBJECTIVE: Determine if the final set of demographic, vital sign, and laboratory data from a hospitalization can be used to accurately quantify 1-year mortality risk. DESIGN: A retrospective study using electronic medical record data linked with the state death registry. PARTICIPANTS: A total of 59,848 hospitalized patients within a six-hospital network over a 4-year period. MAIN MEASURES: The last set of vital signs, complete blood count, basic and complete metabolic panel, demographic information, and ICD codes. The outcome of interest was death within 1 year. KEY RESULTS: Model performance was measured on the validation data set. Random forests (RF) outperformed logisitic regression (LR) models in discriminative ability. An RF model that used the final set of demographic, vitals, and laboratory data from the final 48 h of hospitalization had an AUC of 0.86 (0.85-0.87) for predicting death within a year. Age, blood urea nitrogen, platelet count, hemoglobin, and creatinine were the most important variables in the RF model. Models that used comorbidity variables alone had the lowest AUC. In groups of patients with a high probability of death, RF models underestimated the probability by less than 10%. CONCLUSION: The last set of EMR data from a hospitalization can be used to accurately estimate the risk of 1-year mortality within a cohort of multicondition hospitalized patients.

Diagnostic Discordance, Health Information Exchange, and Inter-Hospital Transfer Outcomes: a Population Study.

BACKGROUND: Studying diagnostic error at the population level requires an understanding of how diagnoses change over time. OBJECTIVE: To use inter-hospital transfers to examine the frequency and impact of changes in diagnosis on patient risk, and whether health information exchange can improve patient safety by enhancing diagnostic accuracy. DESIGN: Diagnosis coding before and after hospital transfer was merged with responses from the American Hospital Association Annual Survey for a cohort of patients transferred between hospitals to identify predictors of mortality. PARTICIPANTS: Patients (180,337) 18 years or older transferred between 473 acute care hospitals from NY, FL, IA, UT, and VT from 2011 to 2013. MAIN MEASURES: We identified discordant Elixhauser comorbidities before and after transfer to determine the frequency and developed a weighted score of diagnostic discordance to predict mortality. This was included in a multivariate model with inpatient mortality as the dependent variable. We investigated whether health information exchange (HIE) functionality adoption as reported by hospitals improved diagnostic discordance and inpatient mortality. KEY RESULTS: Discordance in diagnoses occurred in 85.5% of all patients. Seventy-three percent of patients gained a new diagnosis following transfer while 47% of patients lost a diagnosis. Diagnostic discordance was associated with increased adjusted inpatient mortality (OR 1.11 95% CI 1.10-1.11, p < 0.001) and allowed for improved mortality prediction. Bilateral hospital HIE participation was associated with reduced diagnostic discordance index (3.69 vs. 1.87%, p < 0.001) and decreased inpatient mortality (OR 0.88, 95% CI 0.89-0.99, p < 0.001). CONCLUSIONS: Diagnostic discordance commonly occurred during inter-hospital transfers and was associated with increased inpatient mortality. Health information exchange adoption was associated with decreased discordance and improved patient outcomes.

Does serum procalcitonin aid in the diagnosis of bloodstream infection regardless of whether patients exhibit the systemic inflammatory response syndrome?

BACKGROUND: Physicians frequently rely on the systemic inflammatory response syndrome (SIRS) criteria to detect bloodstream infections (BSIs). We evaluated the diagnostic performance of procalcitonin (PCT) in detecting BSI in patients with and without SIRS. METHODS: We tested the association between BSI, serum PCT levels, contemporaneous SIRS scores and serum lactate using logistic regression in a dataset of 4279 patients. The diagnostic performance of these variables was assessed. RESULTS: In multivariate regression analysis, only log(PCT) was independently associated with BSI (p < 0.05). The mean area under the curve (AUC) of PCT in detecting BSI (0.683; 95% CI 0.65-0.71) was significantly higher than serum lactate (0.615; 95% CI 0.58-0.64) and the SIRS score (0.562; 95% CI 0.53-0.58). The AUC of PCT did not differ significantly by SIRS status. PCT of less than 0.1 ng/mL had a negative predictive value (NPV) of 97.4 and NPV of 96.2% for BSI in the SIRS-negative and SIRS-positive patients, respectively. A PCT of greater than 10 ng/mL had a LR of 6.22 for BSI in SIRS-negative patients. The probability of BSI increased exponentially with rising PCT levels regardless of SIRS status. CONCLUSION: The performance of PCT for the diagnosis of BSI was not affected by SIRS status. Only PCT was independently associated with BSI, while the SIRS criterion and serum lactate were not. A low PCT value may be used to identify patients at a low risk for having BSI in both settings. An elevated PCT value even in a SIRS negative patient should prompt a careful search for BSI.

Can serum Procalcitonin aid in the diagnosis of blood stream infection in patients on immunosuppressive medications?

BACKGROUND: Patients on immunosuppressive medications may not exhibit the systemic inflammatory response syndrome (SIRS) in the setting of bacterial infection. Our study examines the relationship between serum PCT levels and the odds of manifesting SIRS and BSI in patients on immunosuppressive medications and examines whether this relationship is altered from patients who are not on these medications. The diagnostic performance of Procalcitonin (PCT) detecting BSI in patients on immunosuppressive agents is compared to that in non-immunosuppressed patients. METHODS: We tested the association between BSI, serum PCT levels, contemporaneous SIRS scores using logisitic regression in a dataset of 4279 patients. The diagnostic performance of these variables for detecting BSI was assessed. RESULTS: In patients on immunosuppressive medications, multivariate logistic regression models demonstrate that while the serum PCT level is associated with BSI (OR: 2.48, p < .05) - the SIRS score is not. At any given serum PCT level, patients on immunosuppressive agents have lower odds of exhibiting SIRS despite having the same odds of having BSI as non-immunosuppressed patients. PCT (AUC: 0.68) performs better than SIRS (AUC: 0.52) in detecting the presence of BSI in patients on immunosuppressive medications. The diagnostic performance of PCT for detecting BSI in immunosuppressed patients is not significantly different from the non-immunosuppressed cohort. CONCLUSIONS: As PCT levels rise, patients on immunosuppressive agents are less likely to mount a SIRS response, despite having a high probability of BSI. PCT might prove helpful in this setting as immunosuppressive agents do not alter the diagnostic performance of serum PCT in detecting BSI.

Clinical and Sociocultural Factors Associated With Failure to Escalate Care of Deteriorating Patients.

In-hospital medical emergencies occur frequently. Understanding how clinicians respond to deteriorating patients outside the intensive care unit (ICU) could improve "rescue" interventions and rapid response programs. This was a qualitative study with interviews with 40 clinicians caring for patients who had a "Code Blue" activation or an unplanned ICU admission at teaching hospitals over 7 months. Four study physicians independently analyzed interview transcripts; refined themes were linked to the transcript using text analysis software. Nine themes were found to be associated with clinicians' management of deteriorating patients. Multiple human biases influence daily care for deteriorating hospitalized patients. A novel finding is that "moral distress" affects escalation behavior for patients with poor prognosis. Most themes indicate that ward culture influences clinicians to wait until the last minute to escalate care despite being worried about the patients' condition.

Extreme Hyperferritinemia: Causes and Impact on Diagnostic Reasoning.

OBJECTIVES: Hyperferritinemia can be a result of inflammation, infection, chronic iron overload, or other uncommon pathologies including hemophagocytic lymphohistiocytosis (HLH). There is a historical association between extreme hyperferritinemia and HLH, but in reality HLH is associated with a minority of hyperferritinemic states. METHODS: We identified conditions most associated with hyperferritinemia by identifying 65,536 serum ferritin levels at the University of Minnesota Hospital over a five-year period, with 86 values higher than 10,000 ng/mL. Pediatric patients comprised 22% of this population, and adults, 78%. RESULTS: The majority of cases in both populations with hyperferritinemia were due to chronic transfusion (35%), followed by liver disease (27%), and hematologic malignancy (16%). Solid malignancies, infection, macrophage activation syndrome, and primary and secondary HLH comprised the remaining (22%). CONCLUSIONS: Although this supports the relationship between extreme hyperferritinemia and HLH, it maintains that the positive predictive value of hyperferritinemia for HLH is quite low, and one should consider more common explanations before suspecting HLH.