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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.A multicenter retrospective observational study.A seven-hospital network including academic tertiary care center.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.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.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.

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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11606-020-05733-1) contains supplementary material, which is available to authorized users.

Studying diagnostic error at the population level requires an understanding of how diagnoses change over time. 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. 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. Patients (180,337) 18 years or older transferred between 473 acute care hospitals from NY, FL, IA, UT, and VT from 2011 to 2013. 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. 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

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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11606-018-4316-y) contains supplementary material, which is available to authorized users.

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. 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. In multivariate regression analysis, only log(PCT) was independently associated with BSI (p

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.

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.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.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.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.

main purpose of Message Authentication is to prevent manipulation of the message which is sent. MAC stands for Message Authentication Code which is also known as "Integrity Check Value" or "Cryptographic Checksum". The basic objectives of a hash function are to: • Prevent finding a message from a given hash value (Inversion) • Prevent finding two messages with the same hash value (Collision) On the other hand, Message Authentication Codes are mainly to prevent forgery. Thus, using hash functions for Message Authentication may get a bit complex as hash functions do not have the in-built functionality of a key. In this paper, we discuss a few popular cryptographic hashing algorithms and compare their performance with respect to each other. KeywordsAlgorithms, Authentication Code, MD5

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.

This paper gives a detailed account of the steps required to develop a multimedia system, which marries the software component—an arcade-style game developed using Pygame libraries and the hardware component—a portable arcade game machine developed using a Raspberry Pi processor. It also describes the various functionalities Pygame libraries offer to game developers and emphasizes its ease of use along with the benchmark statistics.

paper, we will discuss and review the steps involved in developing an arcade game machine from ground-up along with designing and developing an arcade game to run on it. We will also discuss the choice of hardware and the development tools used for developing our system. In-game physics will also be incorporated using the PyODE engine and Pygame libraries. We will also compare Easel and Pygame as game development libraries and will determine which would be the most appropriate for our project. KeywordsGame, Raspberry Pi

Objective/Aim: To determine the impact of temporal trends in NT-proBNP levels on 6-month survival in HFpEF and HFrEF. Introduction: High levels NT-proBNP are associated with reduced survival patients with heart failure. However, it remains to be conclusively demonstrated that in addition to absolute NT-proBNP levels, temporal trends in NT-proBNP level in individual patients also impact survival. Methods: We conducted a retrospective study on a cohort of 8255 patients who had at least one admission to the hospital with diagnoses of heart failure between Jan 2011 and Dec 2014.Using EMR data, we created a dataset that included all NT-proBNP levels obtained on these patients at any time during the study interval, irrespective of whether the test was done in a hospital or clinic setting. We used text-mining techniques to extract the numerical value of ejection fraction from reports of echocardiograms obtained during this interval. We performed multivariate regression analysis to identify factors independently associated with six-month survival. In addition, we also constructed Kaplan-Meier curves to determine the impact of temporal trends in NT-proBNP in individual patients on 6-month survival. Results: For patients with HFpEF (EF>45%), and HFrEF, maximum NT-proBNP levels achieved during the study period were associated with higher odds of 6-month mortality (p Conclusion: A rising NT-proBNP level is associated with higher 6-month mortality in patients with HFpEF as well as HFrEF. This may indicate that the use of NT-proBNP guided therapy for patients with heart failure merits further study.

Hyperferritinemia can be the result of inflammation, infection, chronic iron overload, or more uncommon and serious pathologies, including hemophagocytic lymphohistiocytosis (HLH). However, the diagnosticity (ability to differentiate between disorders) of extreme hyperferritinemia (ferritin level > 10,000 ng/mL) has been questioned of late in the literature. We performed retrospective analysis that identified 65,536 serum …

High dose chemotherapy followed by autologous hematopoietic cell transplantation (AHCT) is the standard of care for patients with relapsed aggressive Hodgkin lymphoma (HL), non-Hodgkin lymphoma (NHL) and after initial treatment of multiple myeloma (MM). With advances in supportive care, AHCT is increasingly being performed for patients older than 60 years of age. However, prior studies evaluating outcomes of transplant in older AHCT recipients are inconsistent, with conflicting data on risk of complications and major outcomes of non-relapse mortality (NRM), overall survival (OS) and progression free survival (PFS). Data from registry studies report lower NRM in young and old patients, in recent years, likely due to improvement in supportive care. We analyzed patients receiving an AHCT for MM or lymphoma in a contemporary cohort (2010-2012) receiving consistent treatment and supportive care. We hypothesized that with improved supportive care, there would be little or no difference in outcomes of younger (40-59 years) versus older (> 60 years) AHCT recipients, examining engraftment, OS, PFS, NRM and non-hematologic grade 3-5 toxicities within one year post AHCT. Study cohort and Methods: All patients > 40 years (n=144) who underwent an AHCT for lymphoma (NHL, n = 56; HL, n = 11) or MM (n = 77) from 2010 -2012 at University of Minnesota were included. Engraftment, OS, PFS, NRM and non-hematologic grade 3-5 toxicities within the first year post AHCT were compared between the two cohorts (age 40-59 years, > 60 years). Results. The median age at transplant was 54 years (range 40-59 years) in the younger cohort (n= 77) and 65 years (range 60-76 years) in the older cohort (n=67). Older recipients more often had MM and were less often in complete remission. Over 95% of patients in both groups had a Karnofsky performance status (KPS) of > 80% (p=0.46) while 27% in the older group versus 22% in the younger group had a HCT-CI comorbidity score of > 3 (p= 0.72). We examined 17 categories of non-hematologic grade 3-5 toxicities. The frequency of infections during neutropenia was significantly higher in the older group (64% vs. 44%; 0=0.02). The proportion of patients with any grade 3-5 toxicity was also higher in the older group (84% vs. 67%, p= 0.03). In multivariate analysis, after adjustment for gender and disease, older age (> 60) was significantly associated with higher odds ratio (OR: 2.57, 95% CI: [1.09-6.05]) of grade 3-5 toxicity (Table 1). Based on the high-risk variables shown in Table 1, the predicted probability of grade 3-5 toxicities within one year (adjusted for age, gender and disease) was highest (94%) in females > 60 years with NHL and lowest in males age 40-59 years with HL (47%). There was no difference in time to neutrophil and platelet engraftment (Table 2). Two older recipients (cumulative incidence of NRM: 3%) and no younger recipients had NRM within one year. After a median follow up of two years, the probability of OS at two years was lower in the older group (76% vs. 90%, P=0.04) but no difference was noted in disease relapse or PFS. Conclusion: AHCT can be performed safely in older recipients. However, the higher toxicity and slightly higher NRM in this population needs attention. Studies focusing on risk-stratification in older patients (including geriatric assessments) would further help predict toxicity. Further studies addressing enhanced supportive care needs for older patients who are most likely to benefit are indicated. Table 1: Risk factors for grade 3-5 toxicities Factor OR 95% CI P Age 40-59 1.00 Age 60-76 2.57 1.09 – 6.05 .03 Male 1.00 Female 2.24 0.96 – 5.24 .06 Multiple myeloma 1.00 Hodgkin lymphoma 0.82 0.21 – 3.18 .77 Non-Hodgkin lymphoma 2.67 1.07 – 6.68 .04 Table 2: Outcomes by age Age 40-59 Age 60+ P Days to ANC recovery median (IQR) 10 (10-11) 11 (10-11) 0.15 Days to Platelet > 20K median (IQR) 19 (17-21) 18 (17-21) 0.86 1 year NRM (95% CI) 0 3 (1-7) 0.05 2 year relapse (95% CI) 47 (34-60) 39 (27-51) 0.44 2 year OS (95% CI) 90 (82-98) 76 (65-87) 0.04 2 year PFS (95% CI) 53 (41-65) 55 (43-67) 0.80 Disclosures Bachanova: Seattle Genetics, Inc.: Consultancy, Research Funding.